MedSyn: Enhancing Diagnostics with Human-AI Collaboration

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.

@article{sayin2025_2506.14774,
  title={ MedSyn: Enhancing Diagnostics with Human-AI Collaboration },
  author={ Burcu Sayin and Ipek Baris Schlicht and Ngoc Vo Hong and Sara Allievi and Jacopo Staiano and Pasquale Minervini and Andrea Passerini },
  journal={arXiv preprint arXiv:2506.14774},
  year={ 2025 }
}