Cognitive communications, collaboration, consultation, and instruction with adaptive agentic AI, augmented generative intelligence and neurosynaptic cognition networks

1 . A cognitive communications system enabling multichannel multiplexed communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during collaboration sessions, practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, the cognitive communications system enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, including recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, allowing each participant cognitive collaborant to capture, retrieve and concurrently view at least one source of streaming medical modality imagery data, and at least one or more sources of heterogeneous streaming imagery data, medical and non-medical streaming imagery data, multimodal media and combinations thereof, including images, video, modality imagery, audio, video and haptic wave forms and files, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, both live and archived streaming imagery data, enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data in collaboration sessions practiced by and among at least one or more participant cognitive collaborants during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, each participant cognitive collaborant able to view, curate, annotate and tag the heterogeneous streaming imagery data, comprising a tele-visual imagery informatics management system including, at least one or more tele-visual imagery informatics management system clini-docks, wherein each clini-dock is adapted for independent acquisition and transmission of signals from other sources of streaming imagery data at native, enhanced or reduced resolutions and native enhanced or reduced frame rates, used for the acquisition and transmission of, live or archived streaming imagery data, including images, video, modality imagery, audio, video and haptic wave forms and files, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, analog or digital video signals in standard or non-standard resolutions, medical or non-medical imagery, in compressed or uncompressed imagery formats; at least one or more tele-visual imagery informatics management system clini-pod network servers, wherein each server is a neurosynaptic network node comprising at least one streamer, splitter, router, server and storage device enabling at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, to concurrently view, communicate, collaborate, consult and instruct among participant cognitive collaborants using at least one or more sources of streaming imagery data acquired and transmitted by tele-visual imagery informatics management system clini-docks, including live streaming imagery data, archived streaming imagery data, appended streaming imagery metadata, including appended semantic metadata and annotations, cognitive collaborant annotations, and archived collaborated imagery files during a synchronous or asynchronous collaboration session, establishing and maintaining channel communications for each and all of the one or more sources of streaming imagery data each participant cognitive collaborant wishes to view, monitor and collaborate with, enabling concurrent collaboration including viewing, curation, annotation and tagging with each and all of the one or more sources of streaming imagery data acquired and transmitted by tele-visual imagery informatics management system clini-docks, enables at least one or more participant cognitive collaborants to concurrently view, communicate, collaborate, consult and instruct, including curation, annotation and tagging, with live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files during a synchronous or asynchronous collaboration session, enables at least one or more participant cognitive collaborant in multiple locations, some of whom may be located remotely to the sources of streaming imagery data, to concurrently view, communicate, collaborate, consult and instruct, including curation, annotation and tagging, with live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files during a synchronous or asynchronous collaboration session, enables at least one or more participant cognitive collaborant to independently add sources of streaming imagery data, adjust, enhance or reduce resolutions or frame rates of streaming imagery data with a multi-channel communications control interface, and independently view those additional channels of streaming imagery data and independently select which of those channels to bring into a collaboration session, conveys instructions with two way communications among participant cognitive collaborants, including source channel selection, for viewing, curating, annotating and tagging imagery data streams with telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, encapsulates and saves collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, stores collaborated imagery files from all participant cognitive collaborants locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, retrieves collaborated imagery files from all participant cognitive collaborants stored locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, publishes and distributes collaborated imagery files in known native, single file format structures, including those used for digital imaging and communications in medicine comprising both core and non-core data element tags, together with conformance statements that enable prior evaluation and testing of streaming imagery equipment components without an actual physical connection, all of which facilitate network connectivity for imagery equipment components, communication interoperability for imagery data systems, and exchange of collaborated imagery files, dynamically manages and controls with at least one or more associated databases, authorization, authentication, identity management, security, and access, publication and distribution privileges for viewing, communicating, collaborating, consulting and instructing, and cognitive collaborant privileges, including curation, annotation, tagging, encapsulation, saving, storage, retrieval and distribution of live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files for each participant cognitive collaborant during collaboration sessions, including managing and controlling security tokens providing access for cognitive collaborants maintained in security metadata repositories, blockchain metadata repositories and blockchain data ledgers, enables both synchronous and asynchronous bidirectional communications with at least one or more local area networks, at least one or more wide area networks (internet) including imagery data repositories and combinations thereof during multiple collaboration sessions, enables identification, tracking and monitoring of participant cognitive collaborants by assignment of unique colors for annotations of streaming imagery data, archived collaborated imagery files and cognitive collaborant annotations, that include telestrations, drawings, illustrations, alpha-numeric text annotations, as well as cognitive collaborant annotations combined with alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, enables colorized telestration, annotation and masking of colorized attention maps and colorized prediction bases for explainable artificial intelligence by participant cognitive collaborants by assignment of unique colors for annotations of streaming imagery data, archived collaborated imagery files and cognitive collaborant annotations, including telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, costs annotations, resource consumption annotations and resource utilization annotations; and at least one or more tele-visual imagery informatics management system clini-ports that allows for multiple participant cognitive collaborants, each of whom can capture live streaming imagery data together with associated imagery metadata, including semantic metadata and annotations, and bring into the collaboration session, retrieve archived streaming imagery data together with associated imagery metadata, including semantic metadata and annotations, and bring into the collaboration session, concurrently view, communicate, collaborate, consult and instruct with streaming imagery data, enables curation, annotation and tagging that streaming imagery data with collaborated annotations that include telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, enables encapsulation and saving collaborated streaming imagery data and archived imagery metadata, including archived semantic metadata and annotations, together with appended imagery metadata, including appended semantic metadata and annotations, and collaborated annotations and from each collaboration session, including asynchronous or synchronous collaboration with at least one or more participant cognitive collaborants, in native, single file format structures, known as collaborated imagery files, enables multimodal clinical communications, collaboration, consultation and instruction, including viewing, curating, annotating and tagging, using at least one or more sources of streaming imagery data shared among at least one or more participant cognitive collaborants with a multi-channel stream viewer that enables capture, retrieval and concurrent viewing of both live and archived medical imagery streams together with associated metadata during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, enables independently adding sources of streaming imagery data, adjust, enhance or reduce resolutions or frame rates of streaming imagery data with a multi-channel communications control interface, and independently view those additional channels of streaming imagery data and independently select which of those channels to bring into a collaboration session, enables conveying instructions with two way communications among participant cognitive collaborants, including source channel selection, for viewing, curating, annotating and tagging imagery data streams with telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, and not reliant upon any external communications network.

2 . The cognitive communications system of claim 1 , wherein the tele-visual imagery informatics management system clini-pod network servers are modular and scalable clusters of gateway streamer servers configured to support multiple network topologies, including peer-to-peer, hub-and-spoke, mesh chord and core-spine-leaf networks, as well as in 2-tier, 3-tier, or N-tier application architectures, and heterogeneous network combinations thereof, each gateway streamer server enabling neurosynaptic network connectivity enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, that includes viewing, curating, annotating and tagging, using at least one or more sources of multichannel, multiplexed heterogeneous streaming imagery data, including both medical and non-medical streaming imagery data, multimodal media and combinations thereof, and together with images, video, modality imagery, waveforms, audio and haptic files, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs.

3 . The cognitive communications system of claim 1 for the acquisition and transmission of medical streaming imagery data, including medical images, medical video, medical modality imagery, medical wave form imagery, clinical maps, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, the cognitive communications system preserving the clinical integrity of medical streaming imagery data from medical devices, systems and equipment cleared for medical use, including clinical diagnostic purposes, care delivery and patient monitoring, enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during collaboration sessions, practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, including viewing, curating, annotating and tagging streaming medical imagery data during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, encapsulating and saving collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, storing collaborated imagery files from all participant cognitive collaborants locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, retrieving collaborated imagery files from all participant cognitive collaborants stored locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, publishing and distributing collaborated imagery files in known native, single file format structures, including those used for digital imaging and communications in medicine comprising both core and non-core data element tags, together with conformance statements that enable prior evaluation and testing of streaming imagery equipment components without an actual physical connection, all of which facilitate network connectivity for imagery equipment components, communication interoperability for imagery data systems, and exchange of collaborated imagery files, that can be viewed, curated, annotated, tagged, encapsulated and saved together as collaborated medical imagery files and cleared for use with approved medical devices, equipment, systems, image and data repositories, in native, single file format structures, including those compliant with standards for digital imaging and communications in medicine.

4 . The cognitive communications system of claim 1 for acquiring, transmitting, encapsulating, saving, storing, retrieving, publishing and distributing collaborated imagery files, including live or archived collaborated imagery files, collaborated annotations and tags, together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs.

5 . The cognitive communications system of claim 1 for enabling the encapsulation and saving collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, during collaboration sessions practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof.

6 . The cognitive communications system of claim 1 , wherein the system is adapted for non-clinical applications, including education, finance, and manufacturing, by processing non-medical streaming data with recursive cognitive enrichment for knowledge exchange in heterogeneous networked teams.

7 . The system of claim 1 , incorporating retrieval-augmented generation (RAG) primitives for semantic metadata retrieval and augmentation in collaborative sessions, predating contemporary Graph RAG by integrating vismemes with hypergraph knowledge representations.

8 . The system of claim 1 for agentic AI orchestration, comprising: heterogeneous teams of AI agents for problem-solving, with viability scoring and Graph RAG integration for differentiated search in non-biomedical contexts.

9 . The system of claim 1 , utilizing clinical cognitive vismemes as tokenized assets for knowledge trading in non-PHI environments, extending to NFT-like structures for education and finance.

10 . The system of claim 1 for integrated business architectures (IBAs), incorporating AI-driven metrics for revenues, quality, and service optimization in cross-industry applications.

11 . The system of claim 1 for tokenized economies, using vismemes as NFTs for secure knowledge trading across collaborative networks in education and manufacturing.

12 . The system of claim 1 for change resilience frameworks, sensing/rewiring/locking in learnings with cybernetic teams for AI-induced adaptations in business.

13 . The system of claim 1 , incorporating hypergraph models for spatial multiomics-inspired data in non-clinical analytics, with RAG for precision decision-making.

14 . The system of claim 1 for outcomes performance evaluation, extending clinical analytics to non-healthcare with KPIs for resource utilization and costs.

15 . The system of claim 1 for live multicasting in non-clinical collaborations, with secure vismemes for accelerated knowledge exchange in teams.

16 . The cognitive communications system of claim 1 , further comprising a multi-agent framework for creative problem-solving in non-biomedical contexts, comprising: emergent self-organizing teams of networked minds, machines, languages and tools that purposefully adapt, collaborate and innovate, with human-AI hybrids for differentiated search; viability scoring with Graph RAG integration.

17 . The cognitive communications system of claim 1 , adapted for non-PHI workflows in business architectures, comprising: emergent self-organizing teams of networked minds, machines, languages and tools that purposefully adapt, collaborate and innovate, with semantic knowledge graphs for multi-domain data fusion; Graph RAG for decision support in finance/engineering.

18 . The system of claim 17 , incorporating personal knowledge repositories (PKRs) for documenting best practices in education/business, extending clinical vaults to non-healthcare.

19 . The cognitive communications system of claim 1 , comprising an integrated business architecture (IBA) framework with emergent self-organizing teams of networked minds, machines, languages and tools that purposefully adapt, collaborate and innovate, with AI-driven metrics, enhancing revenues/quality/service in cross-industry applications.

20 . The system of claim 1 , incorporating biomolecular recognition techniques for multiomic data processing, drawing from Mershin's nano-bio interfaces for enhanced cognitive collaboration in biological and synthetic systems.

21 . The system of claim 1 for digital biology applications, inspired by Jensen Huang's vision, using augmented generative AI for precision medicine simulations in computer-assisted drug design.

22 . The system of claim 1 , utilizing cybernetic teammate frameworks from HBS WP 25-043 for human-AI hybrid collaboration in resilience-building scenarios.

23 . The system of claim 1 for virtuous circles in value network nodes (VNN), enabling recursive knowledge exchange with performance indicators for operational analytics.

24 . The system of claim 1 , incorporating Mershin's group research on bio-nano recognition for haptic and multisensory annotations in augmented reality sessions.

25 . The system of claim 1 for JPM Healthcare 2025 alignments, with Huang's digital biology for radiopathomic and morpholomic data in cognitive enrichment.

26 . The system of claim 1 for tokenized vismemes in blockchain ledgers, extending to NFT structures for secure multi-domain knowledge trading.

27 . The system of claim 1 for integrated business architectures with AI metrics, optimizing revenues and quality in finance and manufacturing.

28 . The system of claim 1 for hypergraph-based multiomics-inspired analytics, with RAG for precision in non-clinical decision support.

29 . The system of claim 1 for KPIs in non-healthcare outcomes, extending clinical analytics to resource and cost evaluation.

30 . The system of claim 1 for secure live multicasting, using vismemes for accelerated team knowledge exchange in non-clinical settings.

31 . The system of claim 1 for informatics innovation in manufacturing, optimizing workflows with multimodal communications.

32 . The system of claim 1 for Huang's state of AI in healthcare trends, applying to digital biology for radiogenomic mapping.

33 . The system of claim 1 for resilience frameworks, locking in learnings with governance in heterogeneous networked teams.

34 . The system of claim 1 for primitives in provisionals, integrating vismemes for hypergraph representations in RAG.

35 . The system of claim 1 for multi-agent problem-solving, with human-AI hybrids for creative non-biomedical contexts.

36 . The system of claim 1 for change resilience, using generative AI for early signal sensing in cybernetic teams.

37 . The system of claim 1 for IBAs with metrics, enhancing service in cross-industry AI applications.

38 . The system of claim 1 for non-biomedical viability scoring, integrating Graph RAG in multi-agent frameworks.

39 . The system of claim 1 for federated model training, privacy-preserving across non-medical repositories.

40 . The system of claim 1 for multicasting in collaborations, secure for knowledge acceleration in teams.

41 . The system of claim 1 for re-skilling knowledge workers, comprising multimodal media for adaptive learning in AI-augmented environments with See-One-Do-One-Teach-One pedagogy.

42 . The system of claim 1 for data stream visualizations, comprising real-time monitoring of cognitive collaboration sessions with performance metrics and analytics dashboards rendered as vismemes.

43 . The system of claim 1 for informatics innovation management, comprising workflow optimization with multimodal communications, recursive enrichment, and operational analytics for continuous improvement.

44 . The system of claim 1 for agentic task allocation, comprising autonomous distribution of cognitive collaboration tasks among heterogeneous AI agents with viability scoring across value network nodes.

45 . The system of claim 1 for Graph-RAG retrieval in agentic workflows, comprising differentiated semantic search with viability scoring across knowledge repositories connected as value network nodes.

46 . The system of claim 1 for human-AI hybrid teams, comprising collaborative problem-solving with emergent intelligence in creative contexts across a neurosynaptic network of institutions.

47 . The system of claim 1 for spatial multiomic integration, comprising tissue architecture analysis with molecular profiling for precision diagnostics.

48 . The system of claim 1 for radiopathomic correlation, comprising integration of radiological and pathological data streams for comprehensive diagnosis.

49 . The system of claim 1 for precision therapeutics targeting, comprising multiomic-guided treatment selection with augmented generative AI.

50 . The system of claim 1 for patient stratification, comprising multiomic clustering for optimal treatment subpopulation matching.

51 . The system of claim 1 for explainable AI in clinical decision support, comprising colorized attention masking with interpretable feature visualization as a neurosynaptic trace within the institutional graph.

52 . The system of claim 1 for attention map visualization, comprising highlighting salient features in medical imagery with confidence scoring and storing the resulting attribution maps as vismemes.

53 . The system of claim 1 for regulatory-compliant AI documentation, comprising automated generation of model cards and validation reports for each deployed model instance.

54 . The system of claim 1 for clinical AI validation, comprising continuous monitoring with performance metrics and drift detection for AI services participating in neurosynaptic workflows.

55 . The system of claim 1 for enterprise imaging integration, comprising cognitive collaboration across PACS, VNA, and EHR systems.

56 . The system of claim 1 for DICOM-compliant cognitive vismemes, comprising encapsulation of collaborated imagery with standards-based metadata for interoperable storage, retrieval, and exchange.

57 . The system of claim 1 for cloud-native deployment, comprising N-tier architecture with core-spine-leaf networking for scalable cognitive collaboration across distributed institutions.

58 . A method for multichannel multiplexed communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during collaboration sessions, practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, the method enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, including recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, allowing each participant cognitive collaborant to capture, retrieve and concurrently view at least one source of streaming medical modality imagery data, and at least one or more sources of heterogeneous streaming imagery data, medical and non-medical streaming imagery data, multimodal media and combinations thereof, including images, video, modality imagery, audio, video and haptic wave forms and files, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, both live and archived streaming imagery data, enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data in collaboration sessions practiced by and among at least one or more participant cognitive collaborants during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, each participant cognitive collaborant able to view, curate, annotate and tag the heterogeneous streaming imagery data, the method comprising the steps of acquiring and transmitting signals from other sources of streaming imagery data at native, enhanced or reduced resolutions and native enhanced or reduced frame rates, used for the acquisition and transmission of, live or archived streaming imagery data, including images, video, modality imagery, audio, video and haptic wave forms and files, multiomic data-phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, analog or digital video signals in standard or non-standard resolutions, medical or non-medical imagery, in compressed or uncompressed imagery formats; enabling at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, to concurrently view, communicate, collaborate, consult and instruct among participant cognitive collaborants using at least one or more sources of streaming imagery data acquired and transmitted by tele-visual imagery informatics management system clini-docks, including live streaming imagery data, archived streaming imagery data, appended streaming imagery metadata, including appended semantic metadata and annotations, cognitive collaborant annotations, and archived collaborated imagery files during a synchronous or asynchronous collaboration session, establishing and maintaining channel communications for each and all of the one or more sources of streaming imagery data each participant cognitive collaborant wishes to view, monitor and collaborate with, enabling concurrent collaboration including viewing, curation, annotation and tagging with each and all of the one or more sources of streaming imagery data acquired and transmitted by tele-visual imagery informatics management system clini-docks, enabling at least one or more participant cognitive collaborants to concurrently view, communicate, collaborate, consult and instruct, including curation, annotation and tagging, with live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files during a synchronous or asynchronous collaboration session, enabling at least one or more participant cognitive collaborant in multiple locations, some of whom may be located remotely to the sources of streaming imagery data, to concurrently view, communicate, collaborate, consult and instruct, including curation, annotation and tagging, with live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files during a synchronous or asynchronous collaboration session, enabling at least one or more participant cognitive collaborant to independently add sources of streaming imagery data, adjust, enhance or reduced resolutions or frame rates of streaming imagery data with a multi-channel communications control interface, and independently view those additional channels of streaming imagery data and independently select which of those channels to bring into a collaboration session, conveying instructions with two way communications among participant cognitive collaborants, including source channel selection, for viewing, curating, annotating and tagging imagery data streams with telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, encapsulating and saving collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, storing collaborated imagery files from all participant cognitive collaborants locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, retrieving collaborated imagery files from all participant cognitive collaborants stored locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, publishing and distributing collaborated imagery files in known native, single file format structures, including those used for digital imaging and communications in medicine comprising both core and non-core data element tags, together with conformance statements that enable prior evaluation and testing of streaming imagery equipment components without an actual physical connection, all of which facilitate network connectivity for imagery equipment components, communication interoperability for imagery data systems, and exchange of collaborated imagery files, dynamically managing and controlling with at least one or more associated databases, authorization, authentication, identity management, security, and access, publication and distribution privileges for viewing, communicating, collaborating, consulting and instructing, and cognitive collaborant privileges, including curation, annotation, tagging, encapsulation, saving, storage, retrieval and distribution of live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files for each participant cognitive collaborant during collaboration sessions, including managing and controlling security tokens providing access for cognitive collaborants maintained in security metadata repositories, blockchain metadata repositories and blockchain data ledgers, enabling both synchronous and asynchronous bidirectional communications with at least one or more local area networks, at least one or more wide area networks (internet) including imagery data repositories and combinations thereof during multiple collaboration sessions, enabling identification, tracking and monitoring of participant cognitive collaborants by assignment of unique colors for annotations of streaming imagery data, archived collaborated imagery files and cognitive collaborant annotations, that include telestrations, drawings, illustrations, alpha-numeric text annotations, as well as cognitive collaborant annotations combined with alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, enabling colorized telestration, annotation and masking of colorized attention maps and colorized prediction bases for explainable artificial intelligence by participant cognitive collaborants by assignment of unique colors for annotations of streaming imagery data, archived collaborated imagery files and cognitive collaborant annotations, including telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, costs annotations, resource consumption annotations and resource utilization annotations; allowing for multiple participant cognitive collaborants, each of whom can capture live streaming imagery data together with associated imagery metadata, including semantic metadata and annotations, and bring into the collaboration session, retrieve archived streaming imagery data together with associated imagery metadata, including semantic metadata and annotations, and bring into the collaboration session, concurrently view, communicate, collaborate, consult and instruct with streaming imagery data, enabling curation, annotation and tagging that streaming imagery data with collaborated annotations that include telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, enabling encapsulation and saving collaborated streaming imagery data and archived imagery metadata, including archived semantic metadata and annotations, together with appended imagery metadata, including appended semantic metadata and annotations, and collaborated annotations and from each collaboration session, including asynchronous or synchronous collaboration with at least one or more participant cognitive collaborants, in native, single file format structures, known as collaborated imagery files, enabling multimodal clinical communications, collaboration, consultation and instruction, including viewing, curating, annotating and tagging, using at least one or more sources of streaming imagery data shared among at least one or more participant cognitive collaborants with a multi-channel stream viewer that enables capture, retrieval and concurrent viewing of both live and archived medical imagery streams together with associated metadata during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, enabling independently adding sources of streaming imagery data, adjust, enhance or reduce resolutions or frame rates of streaming imagery data with a multi-channel communications control interface, and independently view those additional channels of streaming imagery data and independently select which of those channels to bring into a collaboration session, enabling conveying instructions with two way communications among participant cognitive collaborants, including source channel selection, for viewing, curating, annotating and tagging imagery data streams with telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, and not reliant upon any external communications network.

59 . The method of claim 58 for the acquisition and transmission of medical streaming imagery data, including medical images, medical video, medical modality imagery, medical wave form imagery, clinical maps, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, the method preserving the clinical integrity of medical streaming imagery data from medical devices, systems and equipment cleared for medical use, including clinical diagnostic purposes, care delivery and patient monitoring, enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during collaboration sessions, practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, including viewing, curating, annotating and tagging streaming medical imagery data during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, encapsulating and saving collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, storing collaborated imagery files from all participant cognitive collaborants locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, retrieving collaborated imagery files from all participant cognitive collaborants stored locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, publishing and distributing collaborated imagery files in known native, single file format structures, including those used for digital imaging and communications in medicine comprising both core and non-core data element tags, together with conformance statements that enable prior evaluation and testing of streaming imagery equipment components without an actual physical connection, all of which facilitate network connectivity for imagery equipment components, communication interoperability for imagery data systems, and exchange of collaborated imagery files, that can be viewed, curated, annotated, tagged, encapsulated and saved together as collaborated medical imagery files and cleared for use with approved medical devices, equipment, systems, image and data repositories, in native, single file format structures, including those compliant with standards for digital imaging and communications in medicine.

60 . The method of claim 58 for acquiring, transmitting, encapsulating, saving, storing, retrieving, publishing and distributing collaborated imagery files, including live or archived collaborated imagery files, collaborated annotations and tags, together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs.

61 . The method of claim 58 for enabling the encapsulation and saving collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, during collaboration sessions practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof.

62 . The method of claim 58 for enabling the storage of collaborated imagery files from all participant cognitive collaborants locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations.

63 . The method of claim 58 for enabling the retrieval of collaborated imagery files from all participant cognitive collaborants stored locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations.

64 . The method of claim 58 for enabling the publication and distribution of collaborated imagery files in known native, single file format structures, including those used for digital imaging and communications in medicine comprising both core and non-core data element tags, together with conformance statements that enable prior evaluation and testing of streaming imagery equipment components without an actual physical connection, all of which facilitate network connectivity for imagery equipment components, communication interoperability for imagery data systems, and exchange of collaborated imagery files.

65 . The method of claim 58 for dynamically managing and controlling with at least one or more associated databases, authorization, authentication, identity management, security, and access, publication and distribution privileges for viewing, communicating, collaborating, consulting and instructing, and cognitive collaborant privileges, including curation, annotation, tagging, encapsulation, saving, storage, retrieval and distribution of live streaming imagery data, archived imagery data, appended imagery metadata, including appended semantic metadata and annotations, collaborated annotations, and archived collaborated imagery files for each participant cognitive collaborant during collaboration sessions, including managing and controlling security tokens providing access for cognitive collaborants maintained in security metadata repositories, blockchain metadata repositories and blockchain data ledgers.

66 . The method of claim 58 for enabling both synchronous and asynchronous bidirectional communications with at least one or more local area networks, at least one or more wide area networks (internet) including imagery data repositories and combinations thereof during multiple collaboration sessions.

67 . The method of claim 58 for enabling identification, tracking and monitoring of participant cognitive collaborants by assignment of unique colors for annotations of streaming imagery data, archived collaborated imagery files and cognitive collaborant annotations, that include telestrations, drawings, illustrations, alpha-numeric text annotations, as well as cognitive collaborant annotations combined with alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations.

68 . The method of claim 58 for enabling colorized telestration, annotation and masking of colorized attention maps and colorized prediction bases for explainable artificial intelligence by participant cognitive collaborants by assignment of unique colors for annotations of streaming imagery data, archived collaborated imagery files and cognitive collaborant annotations, including telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, costs annotations, resource consumption annotations and resource utilization annotations.

69 . The method of claim 58 for allowing for multiple participant cognitive collaborants, each of whom can capture live streaming imagery data together with associated imagery metadata, including semantic metadata and annotations, and bring into the collaboration session, retrieve archived streaming imagery data together with associated imagery metadata, including semantic metadata and annotations, and bring into the collaboration session, concurrently view, communicate, collaborate, consult and instruct with streaming imagery data, enabling curation, annotation and tagging that streaming imagery data with collaborated annotations that include telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, enabling encapsulation and saving collaborated streaming imagery data and archived imagery metadata, including archived semantic metadata and annotations, together with appended imagery metadata, including appended semantic metadata and annotations, and collaborated annotations and from each collaboration session, including asynchronous or synchronous collaboration with at least one or more participant cognitive collaborants, in native, single file format structures, known as collaborated imagery files, enabling multimodal clinical communications, collaboration, consultation and instruction, including viewing, curating, annotating and tagging, using at least one or more sources of streaming imagery data shared among at least one or more participant cognitive collaborants with a multi-channel stream viewer that enables capture, retrieval and concurrent viewing of both live and archived medical imagery streams together with associated metadata during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, enabling independently adding sources of streaming imagery data, adjust, enhance or reduce resolutions or frame rates of streaming imagery data with a multi-channel communications control interface, and independently view those additional channels of streaming imagery data and independently select which of those channels to bring into a collaboration session, enabling conveying instructions with two way communications among participant cognitive collaborants, including source channel selection, for viewing, curating, annotating and tagging imagery data streams with telestrations, drawings, illustrations, alpha-numeric text annotations, image annotations, wave form annotations, voice annotations, video annotations, augmented reality imagery annotations, 3D/4D imagery annotations, haptic annotations, document annotations, outcomes annotations, performance annotations, results annotations, resource consumption annotations, resource utilization annotations and costs annotations, and not reliant upon any external communications network.

70 . The method of claim 58 for the acquisition and transmission of medical streaming imagery data, including medical images, medical video, medical modality imagery, medical wave form imagery, clinical maps, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, the method preserving the clinical integrity of medical streaming imagery data from medical devices, systems and equipment cleared for medical use, including clinical diagnostic purposes, care delivery and patient monitoring, enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data during collaboration sessions, practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, including viewing, curating, annotating and tagging streaming medical imagery data during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, encapsulating and saving collaborated annotations and tags together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, storing collaborated imagery files from all participant cognitive collaborants locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, retrieving collaborated imagery files from all participant cognitive collaborants stored locally in media libraries or image data repositories on their respective computer storage devices, in image data repositories on tele-visual imagery informatics management system servers, in image data repositories on cloud storage devices and locations, in image data repositories on picture archiving and communications systems, in other image data repositories compliant with standards for digital imaging and communications in medicine, or in any other data repository that allows streaming imagery data, annotations and metadata, including semantic metadata and annotations, to be combined in native single file format structures, including in such locations as data containers and data catalogs, clinical data repositories, personalized clinical knowledge repositories, clinical cognitive vismeme vaults and metadata repositories, on premises, as well as on cloud storage devices and locations, publishing and distributing collaborated imagery files in known native, single file format structures, including those used for digital imaging and communications in medicine comprising both core and non-core data element tags, together with conformance statements that enable prior evaluation and testing of streaming imagery equipment components without an actual physical connection, all of which facilitate network connectivity for imagery equipment components, communication interoperability for imagery data systems, and exchange of collaborated imagery files, that can be viewed, curated, annotated, tagged, encapsulated and saved together as collaborated medical imagery files and cleared for use with approved medical devices, equipment, systems, image and data repositories, in native, single file format structures, including those compliant with standards for digital imaging and communications in medicine.

71 . The method of claim 58 for acquiring, transmitting, encapsulating, saving, storing, retrieving, publishing and distributing collaborated imagery files, including live or archived collaborated imagery files, collaborated annotations and tags, together with streaming imagery data, relevant imagery metadata, including semantic metadata and annotations, and appended imagery metadata, including appended semantic metadata and annotations, from the collaboration session in native, single file format structures, known as collaborated imagery files, during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs, that can be viewed, curated, annotated, tagged, encapsulated and saved together as collaborated medical imagery files and cleared for use with approved medical devices, equipment, systems, image and data repositories, in native, single file format structures, including those compliant with standards for digital imaging and communications in medicine.

72 . The method of claim 58 for archived collaborated imagery files that can be retrieved for use together with streaming imagery data during synchronous or asynchronous collaboration sessions, revised, appended, viewed, curated, annotated, tagged, encapsulated and saved in native, single file format structures, including those compliant with standards for digital imaging and communications in medicine, during collaboration sessions practiced by and among at least one or more participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, and made available for use together with streaming imagery data during current or subsequent collaboration sessions.

73 . The method of claim 58 for non-clinical workflows, comprising: receiving and transmitting non-medical streaming data; enabling cognitive collaborants to curate, annotate, and encapsulate data in single file formats for rapid adaptive learning in education or business settings.

74 . The method of claim 58 enabling model context protocols (MCP) for standardized context propagation in agentic AI workflows, with semantic registries for federated interoperability across non-clinical domains.

75 . The method of claim 58 for cybernetic resilience in non-clinical settings, comprising: sensing early signals with generative AI; rewiring resources via networked teams; locking in learnings with governance policies for adaptation.

76 . The method of claim 58 for spatial computing in AR/VR, enabling immersive reskilling with multisensory data stream visualization for specialist skills acquisition in non-medical fields.

77 . The method of claim 58 enabling personal knowledge repositories (PKRs) for documenting best practices in non-healthcare, with searchable vaults for value chain knowledge exchange.

78 . The method of claim 58 for non-biomedical problem-solving, comprising: multi-agent frameworks with human-AI hybrids for creative contexts, integrating Graph RAG for viability scoring.

79 . The method of claim 58 for non-PHI business workflows, using semantic knowledge graphs for multi-domain fusion in finance/engineering decision support.

80 . The method of claim 58 for federated learning in non-medical repositories, enabling privacy-preserving model training across distributed knowledge vaults.

81 . The method of claim 58 for multisensory data exchange in education, using semiotics/semantics/somesthetics for immersive learning with haptic annotations.

82 . The method of claim 58 for recursive cognitive enrichment in business, with value chain integration for knowledge creation/visualization/replication.

83 . The method of claim 58 for informatics-enriched innovation in manufacturing, using multimodal communications for workflow optimization.

84 . The method of claim 58 for change resilience in non-clinical settings, comprising emergent self-organizing teams of networked minds, machines, languages and tools that purposefully adapt, collaborate and innovate, sensing early signals with generative AI; rewiring resources via cybernetic teams; locking in learnings with governance policies.

85 . The method of claim 58 for integrating Mershin's Rosetta Stone approach to translate between biological and digital signals in non-clinical multiomic workflows, enabling recursive enrichment with spatial multiomic data.

86 . The method of claim 58 for HCLS (healthcare and life sciences) optimization, incorporating Huang's AI-driven acceleration for radiogenomic and pathomic data analysis in heterogeneous teams.

87 . The method of claim 58 for re-skilling knowledge workers, based on HBS guide, with multimodal media for adaptive learning in AI-augmented environments.

88 . The method of claim 58 for creating virtuous circles in collaborative networks, integrating multiomic maps and heat maps for workflow optimization in non-medical sectors.

89 . The method of claim 58 for deep dive AI requirements in PMWC25, using Graph RAG for viability scoring in agentic AI for precision decision-making.

90 . The method of claim 58 for BICA-LEAP inspired cognitive architectures, enabling federated interoperability in non-PHI knowledge repositories.

91 . The method of claim 58 for spatial computing with AR/VR, integrating somesthetics for immersive multisensory data visualization in reskilling.

92 . The method of claim 58 for personal clinical knowledge repositories extended to non-healthcare, with searchable vaults for best practices.

93 . The method of claim 58 for privacy-preserving federated learning across distributed vaults, for model training in education and business.

94 . The method of claim 58 for semiotics and semantics in education, with haptic annotations for multisensory immersive learning.

95 . The method of claim 58 for value chain integration in business, with recursive enrichment for knowledge creation and replication.

96 . The method of claim 58 for Mershin's TEDx insights on bio-nano interfaces, enabling wave form and haptic integration in collaboration.

97 . The method of claim 58 for HBS cybernetic teams in adaptation, sensing signals for rewiring resources in AI workflows.

98 . The method of claim 58 for virtuous circles in VNN, using data stream visualizations for performance monitoring.

99 . The method of claim 58 for MCP in agentic AI, with semantic registries for context propagation in federated systems.

100 . The method of claim 58 for semantic knowledge graphs in business, fusing data for decision support in finance.

101 . The method of claim 58 for PKRs in education, documenting practices with searchable cognitive vismeme vaults.

102 . The method of claim 58 for tokenized economies, with vismemes as NFTs for knowledge trading in manufacturing.

103 . The method of claim 58 for non-PHI fusion, using hypergraphs for multi-domain analytics in engineering.

104 . The method of claim 58 for multisensory exchange, semiotics for learning with 3D/4D annotations.

105 . The method of claim 58 for cognitive enrichment, value chain for replication in informatics innovation.

106 . The method of claim 58 for cybernetic teammate integration, comprising human-AI hybrid collaboration for resilience-building scenarios with emergent network organization among cognitive collaborants.

107 . The method of claim 58 for continuous learning pipelines, comprising recursive cognitive enrichment for ongoing professional development with cumulative knowledge accumulation in vismeme vaults.

108 . The method of claim 58 for value chain integration, comprising knowledge creation, visualization, and replication across organizational boundaries with recursive enrichment through value network nodes.

109 . The method of claim 58 for autonomous sub-agent spawning, comprising dynamic creation of specialized agents for complex problem decomposition with Graph-RAG integration.

110 . The method of claim 58 for cybernetic closed-loop resilience, comprising continuous adaptation with feedback integration in agentic systems to reorganize uncertainty into auditable neurosynaptic traces.

111 . The method of claim 58 for agentic workflow orchestration, comprising coordinated execution across heterogeneous AI agents with semantic context propagation through the institutional graph of value network nodes.

112 . The method of claim 58 for morpholomic data processing, comprising morphological feature extraction for machine-learning-based analysis.

113 . The method of claim 58 for radiogenomic analysis, comprising correlation of imaging features with genomic markers for treatment stratification.

114 . The method of claim 58 for clinically actionable biomarker identification, comprising AI-driven discovery from integrated multiomic data streams.

115 . The method of claim 58 for precision medicine information sciences, comprising transformation of multiomic data into personalized treatment recommendations.

116 . The method of claim 58 for AI transparency in cognitive collaboration, comprising audit trails that encode an auditable, revisitable causal structure for each recommendation.

117 . The method of claim 58 for model interpretability, comprising feature-importance ranking with natural language explanations linked to corresponding vismemes.

118 . The method of claim 58 for AI governance in healthcare, comprising policy enforcement with explainable decision logging across value network nodes.

119 . The method of claim 58 for FDA-aligned AI transparency, comprising documentation of training data, model architecture, and validation results in a form suitable for regulatory review.

120 . The method of claim 58 for clinical workflow orchestration, comprising AI-driven task routing with cognitive collaborant assignment along end-to-end clinical workflows.

121 . The method of claim 58 for health information exchange, comprising secure vismeme transmission across institutional boundaries using standardized messaging protocols.

122 . A method enabling multichannel multiplexed communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, with streaming imagery data by participant cognitive collaborants, including persons, machines, devices, neural networks, robots and algorithms, including augmented generative AI algorithms, models and systems, and heterogeneous networked teams composed thereof, with modular and scalable clusters of gateway streamer servers configured to support multiple network topologies, including peer-to-peer, hub-and-spoke, mesh chord and core-spine-leaf networks, as well as in 2-tier, 3-tier, or N-tier application architectures, and heterogeneous network combinations thereof, each gateway streamer server enabling neurosynaptic network connectivity enabling both synchronous and asynchronous multimodal clinical communications, collaboration, consultation and instruction, as well as recursive cognitive enrichment and collaborative knowledge exchange, that includes viewing, curating, annotating and tagging, using at least one or more sources of multichannel, multiplexed heterogeneous streaming imagery data, including both medical and non-medical streaming imagery data, multimodal media and combinations thereof, and together with images, video, modality imagery, waveforms, audio and haptic files, multiomic data—phenotypic, genomic, metabolomic, pathomic, radiomic, radiopathomic, radiogenomic, spatial multiomic and morpholomic data, maps and clinical data sets used in computer-assisted drug design and treatment, biometric maps and movies, hapmaps, heat maps, data stream visualizations, structured reports, interactive media reports, including interactive multimedia reporting, clinical documents and key performance indicators, including indicators of quality of care, as well as indicators of clinical, operational and financial performance, during various stages of medical disease and injury management, including detection, diagnosis, prognosis, treatment, measurement, monitoring and reporting, as well as workflow optimization with operational analytics for outcomes, performance, results, resource utilization, resource consumption and costs.

123 . The network of claim 122 , wherein said emergent self-organizing teams actively learn and purposefully adapt, collaborate, and innovate during task execution with recursive cognitive enrichment across value network nodes.

124 . The network of claim 122 , further comprising adaptive collaborative innovation with emergent network organization for novel problem-solving in dynamic clinical and non-clinical environments.

125 . The network of claim 122 , wherein said emergent self-organizing teams fashion collaborated solutions for novel problems or complex tasks by treating institutions as communication networks and causal graphs among cognitive collaborants.

126 . The network of claim 122 , wherein emergent network organization enables self-organizing networked teams of minds, machines, languages, and tools to share knowledge, language, cognition, and technology through neurosynaptic vismeme workflows.

127 . The network of claim 122 , further comprising multi-parametric, multi-sensory signal intelligence and packetized augmented cognition across a brain-like, small-world, scale-free neurosynaptic network of value network nodes.

128 . The network of claim 122 , wherein said value network nodes form a neurosynaptic institutional network that records auditable, revisitable causal structure for decisions, including human rationales, AI attribution graphs, and workflow histories stored as vismemes.