MedSyn: Enhancing Diagnostics with Human-AI Collaboration
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911046361415680 |
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| author | Sayin, Burcu Schlicht, Ipek Baris Hong, Ngoc Vo Allievi, Sara Staiano, Jacopo Minervini, Pasquale Passerini, Andrea |
| author_facet | Sayin, Burcu Schlicht, Ipek Baris Hong, Ngoc Vo Allievi, Sara Staiano, Jacopo Minervini, Pasquale Passerini, Andrea |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14774 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedSyn: Enhancing Diagnostics with Human-AI Collaboration Sayin, Burcu Schlicht, Ipek Baris Hong, Ngoc Vo Allievi, Sara Staiano, Jacopo Minervini, Pasquale Passerini, Andrea Machine Learning Artificial Intelligence Human-Computer Interaction 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. |
| title | MedSyn: Enhancing Diagnostics with Human-AI Collaboration |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.14774 |