MedSyn: Enhancing Diagnostics with Human-AI Collaboration

Understand

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.