Human-Centered AI in Multidisciplinary Medical Discussions: Evaluating the Feasibility of a Chat-Based Approach to Case Assessment

In this study, we investigate the feasibility of using a human-centered artificial intelligence (AI) chat platform where medical specialists collaboratively assess complex cases. As the target population for this platform, we focus on patients with cardiovascular diseases who are in a state of multimorbidity, that is, suffering from multiple chronic conditions. We evaluate simulated cases with multiple diseases using a chat application by collaborating with physicians to assess feasibility, efficiency gains through AI utilization, and the quantification of discussion content. We constructed simulated cases based on past case reports, medical errors reports and complex cases of cardiovascular diseases experienced by the physicians. The analysis of discussions across five simulated cases demonstrated a significant reduction in the time required for summarization using AI, with an average reduction of 79.98%. Additionally, we examined hallucination rates in AI-generated summaries used in multidisciplinary medical discussions. The overall hallucination rate ranged from 1.01% to 5.73%, with an average of 3.62%, whereas the harmful hallucination rate varied from 0.00% to 2.09%, with an average of 0.49%. Furthermore, morphological analysis demonstrated that multidisciplinary assessments enabled a more complex and detailed representation of medical knowledge compared with single physician assessments. We examined structural differences between multidisciplinary and single physician assessments using centrality metrics derived from the knowledge graph. In this study, we demonstrated that AI-assisted summarization significantly reduced the time required for medical discussions while maintaining structured knowledge representation. These findings can support the feasibility of AI-assisted chat-based discussions as a human-centered approach to multidisciplinary medical decision-making.