Multi-agent orchestrated artificial-intelligence system for medical imaging analysis and clinical decision support

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.

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.