MedSyn: Enhancing Diagnostics with Human-AI Collaboration

• URL: http://arxiv.org/abs/2506.14774v2
• Date: Tue, 08 Jul 2025 21:35:13 GMT
• Title: MedSyn: Enhancing Diagnostics with Human-AI Collaboration
• Authors: Burcu Sayin, Ipek Baris Schlicht, Ngoc Vo Hong, Sara Allievi, Jacopo Staiano, Pasquale Minervini, Andrea Passerini,
• Abstract summary: Large Language Models (LLMs) have shown promise as tools for supporting clinical decision-making.<n>We propose a hybrid human-AI framework, MedSyn, where physicians and LLMs engage in multi-step, interactive dialogues to refine diagnoses and treatment decisions.
• Score: 19.23358929400838
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Clinical decision-making is inherently complex, often influenced by cognitive biases, incomplete information, and case ambiguity. Large Language Models (LLMs) have shown promise as tools for supporting clinical decision-making, yet their typical one-shot or limited-interaction usage may overlook the complexities of real-world medical practice. In this work, we propose a hybrid human-AI framework, MedSyn, where physicians and LLMs engage in multi-step, interactive dialogues to refine diagnoses and treatment decisions. Unlike static decision-support tools, MedSyn enables dynamic exchanges, allowing physicians to challenge LLM suggestions while the LLM highlights alternative perspectives. Through simulated physician-LLM interactions, we assess the potential of open-source LLMs as physician assistants. Results show open-source LLMs are promising as physician assistants in the real world. Future work will involve real physician interactions to further validate MedSyn's usefulness in diagnostic accuracy and patient outcomes.

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