MedSyn: Enhancing Diagnostics with Human-AI Collaboration
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 7 May 2025.
Builds on
25 of 37 references.
References · 25
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13 more
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Citing papers per year: 2026: 2.
2 papers.