Explainable Multi-Agent Clinical Intelligence Framework for Advanced Healthcare Decision Support
DOI:
https://doi.org/10.64137/31079377/IJMSD-V2I3P103Keywords:
Multi-Agent Clinical Reasoning, Explainable Artificial Intelligence (XAI), Healthcare Decision Intelligence, Clinical Decision Support Systems, Intelligent Healthcare Agents, Explainable Clinical Analytics, Collaborative AI in Healthcare, Medical Decision-Making, Human-Centered Healthcare AI, Trustworthy Clinical Intelligence SystemsAbstract
Clinical reasoning in medicine must integrate increasing knowledge with established guidelines while considering patient-specific factors. AI, expert systems, and recommender systems support clinicians, but explaining the reasons for the computer’s decision remains a major challenge. Simulation environments enable researchers to explore the potential of different Clinical Reasoning Models in a Multi-Agent System environment. Tests in simulated environments are supported by real-world trials in which agents collaborate, patients are modeled with knowledge and need representations, and preventive and therapeutic prescriptions constitute another dimension of the process. Most frameworks introduce Multi-Agent Clinical Reasoning Systems that are able to collaborate for Explainable Decision Intelligence in Healthcare. Roles are introduced together with the specific characteristics of the medical agents. Decision interpretation and explanation requirements are analyzed for a diagnostic use case supported by a differential framework, where a clinical hypothesis must be confirmed or refuted, taking into account all the evidence from available knowledge. Evaluation, planning, and recommendation agents reason concerning patient evolution through time and suggest preventive or therapeutic prescriptions.
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