The Optimization Paradox in Clinical AI Multi-Agent Systems

• URL: http://arxiv.org/abs/2506.06574v2
• Date: Thu, 12 Jun 2025 02:19:49 GMT
• Title: The Optimization Paradox in Clinical AI Multi-Agent Systems
• Authors: Suhana Bedi, Iddah Mlauzi, Daniel Shin, Sanmi Koyejo, Nigam H. Shah,
• Abstract summary: The relationship between component-level optimization and system-wide performance remains poorly understood.<n>We evaluated this relationship using 2,400 real patient cases from the MIMIC-CDM dataset.<n>Our results reveal a paradox: while multi-agent systems generally outperformed single agents, the component-optimized or Best of Breed system with superior components and excellent process metrics significantly underperformed in diagnostic accuracy (67.7% vs. 77.4% for a top multi-agent system)
• Score: 13.177792688650971
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent artificial intelligence systems are increasingly deployed in clinical settings, yet the relationship between component-level optimization and system-wide performance remains poorly understood. We evaluated this relationship using 2,400 real patient cases from the MIMIC-CDM dataset across four abdominal pathologies (appendicitis, pancreatitis, cholecystitis, diverticulitis), decomposing clinical diagnosis into information gathering, interpretation, and differential diagnosis. We evaluated single agent systems (one model performing all tasks) against multi-agent systems (specialized models for each task) using comprehensive metrics spanning diagnostic outcomes, process adherence, and cost efficiency. Our results reveal a paradox: while multi-agent systems generally outperformed single agents, the component-optimized or Best of Breed system with superior components and excellent process metrics (85.5% information accuracy) significantly underperformed in diagnostic accuracy (67.7% vs. 77.4% for a top multi-agent system). This finding underscores that successful integration of AI in healthcare requires not just component level optimization but also attention to information flow and compatibility between agents. Our findings highlight the need for end to end system validation rather than relying on component metrics alone.

Related papers

• Collaborative Medical Triage under Uncertainty: A Multi-Agent Dynamic Matching Approach [4.474709234869498]
  Post-pandemic surge in healthcare demand, coupled with critical nursing shortages, has placed unprecedented pressure on medical triage systems.<n>We present a multi-agent interactive intelligent system for medical triage that addresses three fundamental challenges in current AI-based triage systems.
  arXiv  Detail & Related papers  (2025-07-30T09:21:59Z)
• GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems [1.7825757481227436]
  We introduce GEMMAS, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph.<n>To capture collaboration quality, we propose two process-level metrics: Information Diversity Score (IDS) to measure semantic variation in inter-agent messages, and Unnecessary Path Ratio (UPR) to quantify redundant reasoning paths.<n>We evaluate GEMMAS across five benchmarks and highlight results on GSM8K, where systems with only a 2.1% difference in accuracy differ by 12.8% in IDS and 80% in UPR, revealing substantial variation in internal collaboration.
  arXiv  Detail & Related papers  (2025-07-17T14:59:20Z)
• An Agentic System for Rare Disease Diagnosis with Traceable Reasoning [58.78045864541539]
  We introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM)<n>DeepRare generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning.<n>The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases.
  arXiv  Detail & Related papers  (2025-06-25T13:42:26Z)
• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)
• TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews [54.35097932763878]
  Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data.<n>Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis framework using Multi-Agent LLMs for clinical interviews.<n>We demonstrate that TAMA outperforms existing LLM-assisted TA approaches, achieving higher thematic hit rate, coverage, and distinctiveness.
  arXiv  Detail & Related papers  (2025-03-26T15:58:16Z)
• Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance [0.0]
  We compare novel agent system designs that use modular agents to analyze laboratory results, vital signs, and clinical context.<n>We implement our agent system with the eICU database, including running lab analysis, vitals-only interpreters, and contextual reasoners agents.
  arXiv  Detail & Related papers  (2025-03-25T05:32:43Z)
• Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking [58.25862290294702]
  We present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow.<n>We also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses.
  arXiv  Detail & Related papers  (2024-12-02T15:25:02Z)
• AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow [33.8495939261319]
  We develop an advanced simulated patient system with AIPatient Knowledge Graph (AIPatient KG) as the input and Reasoning Retrieval-Augmented Generation (Reasoning RAG) as the generation backbone. Reasoning RAG leverages six LLM powered agents spanning tasks including retrieval, KG query generation, abstraction, checker, rewrite, and summarization. Our system also presents high readability (median Flesch Reading Ease 77.23; median Flesch Kincaid Grade 5.6), robustness (ANOVA F-value 0.6126, p>0.1), and stability (ANOVA F-value 0.782, p>0.1)
  arXiv  Detail & Related papers  (2024-09-27T17:17:15Z)
• Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology [0.6397820821509177]
  We introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine. This engine autonomously coordinates and deploys a set of specialized medical AI tools. We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases.
  arXiv  Detail & Related papers  (2024-04-06T15:50:19Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias [5.421033429862095]
  Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. This study explores the role of large language models in mitigating these biases through the utilization of a multi-agent framework.
  arXiv  Detail & Related papers  (2024-01-26T01:35:50Z)
• Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability [63.05238390013457]
  We describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign. Our approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. Results show that the proposed approach outperforms all the rule-based fusion schemes.
  arXiv  Detail & Related papers  (2022-11-24T12:11:22Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.