Multi-agent orchestrated artificial-intelligence system for medical imaging analysis and clinical decision support
Disclosed herein are computer-implemented method, system, and non-transitory computer-readable device aspects for orchestrated multi-agent medical imaging analysis. The disclosed system receives medical imaging data, performs standardized preprocessing, and applies one or more machine-learning inference models to generate intermediate diagnostic outputs. An orchestration agent dynamically selects and invokes specialized downstream agents, including explainability, segmentation, clinical-context fusion, risk scoring, and bias detection agents, based on workflow rules, confidence thresholds, and shared-state memory conditions. The system then generates a structured clinical output comprising diagnostic measurements, visual overlays, narrative explanations, and risk assessments. The multi-agent architecture eliminates fragmented diagnostic pipelines and enables adaptive, reliable, and interpretable clinical decision support. The system further improves robustness by supporting fallback inference, self-healing agent behaviors, and parallel execution paths.
1 . A computer-implemented method for orchestrated medical-imaging analysis, comprising:
receiving, by a scan intake component executed by one or more processors, medical imaging data;
preprocessing the medical imaging data to generate preprocessed data;
performing inference on the preprocessed data using one or more machine-learning models to generate inference outputs, wherein the inference outputs include one or more attribution tensors;
updating a shared-state memory object to store the inference outputs and associated metadata;
selecting, by an orchestration agent, one or more specialized agents from a plurality of specialized agents based on workflow-graph logic and one or more confidence values associated with the inference outputs stored in the shared-state memory object, wherein the one or more specialized agents includes an explainability agent;
executing the one or more selected specialized agents to generate one or more corresponding agent outputs, the executing comprising:
updating the shared-state memory object to store the one or more agent outputs including one or more explainability-related artifacts generated by the explainability agent; and
continuing, by the orchestration agent, execution of the one or more selected specialized agents based on the one or more agent outputs stored in the shared-state memory object and the workflow-graph logic, and
generating a clinical result based on the one or more agent outputs.
2 . The method of claim 1 , wherein preprocessing comprises at least one of voxel normalization, noise reduction, resampling, or orientation alignment.
3 . The method of claim 1 , wherein performing inference comprises generating segmentation masks, probability volumes, or bounding regions.
4 . The method of claim 1 , wherein the orchestration agent selects the specialized agents based on a confidence distribution, uncertainty score, or metadata completeness indicator.
5 . The method of claim 1 , wherein the explainability agent is configured to generate an attention-based visual overlay or a natural-language explanation.
6 . The method of claim 1 , wherein executing the specialized agents comprises invoking a bias-detection agent configured to compute scanner-based or demographic-based fairness metrics.
7 . The method of claim 1 , wherein the orchestration agent re-invokes inference using an alternative neural-network model responsive to a low-confidence inference result.
8 . The method of claim 1 , wherein the shared-state memory object comprises tensors, timestamps, provenance identifiers, or workflow-state indicators.
9 . A medical-imaging analysis system comprising one or more processors and memory storing instructions that, when executed, cause the system to:
receive medical imaging data in a scan intake agent;
preprocess the imaging data in a preprocessing agent;
perform inference using a trained neural-network model to generate inference outputs, wherein the inference outputs include one or more attribution tensors;
store the inference outputs in a shared-state memory object;
select, by an orchestration agent, one or more specialized agents from a plurality of specialized agents based on workflow-graph routing logic and confidence values associated with the inference outputs stored in the shared-state memory object, wherein the one or more specialized agents includes an explainability agent;
execute the selected specialized agents, wherein the execution of the selected specialized agents comprises:
storing one or more agent outputs of the selected specialized agents in the shared-state memory object, wherein the one or more agents outputs include one or more explainability-related artifacts generated by the explainability agent; and
continuing, by the orchestration agent, execution of the selected specialized agents based on the one or more agent outputs stored in the shared-state memory object and the workflow-graph logic; and
generate a clinical output based on outputs of the selected specialized agents.
10 . The system of claim 9 , wherein the plurality of specialized agents comprises modular components accessible via inter-process communication or application-programming interfaces.
11 . The system of claim 9 , wherein the orchestration agent executes the selected specialized agents sequentially or in parallel according to workflow-graph conditions.
12 . The system of claim 9 , further comprising a bias-detection agent configured to compute fairness metrics using demographic-stratified error analysis.
13 . The system of claim 9 , wherein the explainability agent is configured to generate heatmap-based visualizations.
14 . The system of claim 9 , wherein the orchestration agent invokes a fallback inference model having a distinct architecture or training dataset.
15 . The system of claim 9 , wherein the workflow-graph comprises nodes corresponding to individual agents and edges specifying routing conditions.
16 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the processors to:
receive medical imaging data;
perform preprocessing to generate normalized imaging data;
apply machine-learning inference to generate inference outputs, wherein the inference outputs include one or more attribution tensors;
update a shared workflow state to store the inference outputs and metadata values;
invoke, using an orchestration agent, one or more specialized agents selected according to workflow-graph rules and confidence values associated with the inference outputs stored in the shared-state memory object, wherein the one or more specialized agents includes an explainability agent, the invocation comprising:
storing one or more agent outputs of the one or more specialized agents in the shared-state memory object, wherein the one or more agent outputs includes one or more explainability-related artifacts generated by the explainability agent; and
continuing, by the orchestration agent, execution of the one or more specialized agents based on the one or more agent outputs stored in the shared-state memory object and the workflow-graph logic; and
generate a structured clinical result based on outputs of the specialized agents.
17 . The non-transitory computer-readable medium of claim 16 , wherein the instructions further cause the processors to compute quality metrics comprising at least a signal-to-noise ratio, slice-continuity score, or artifact-presence score.
18 . The non-transitory computer-readable medium of claim 16 , wherein the specialized agents comprise at least the explainability agent, a clinical-context agent, or a risk-scoring agent.
19 . The non-transitory computer-readable medium of claim 16 , wherein the instructions further cause the processors to validate consistency between segmentation outputs and natural-language explanations.
20 . The non-transitory computer-readable medium of claim 16 , wherein generating the structured clinical result comprises aggregating outputs from at least three specialized agents.