Towards a Medical AI Scientist
Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this work, we introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research. It enables clinically grounded ideation by transforming extensively surveyed literature into actionable evidence through clinician-engineer co-reasoning mechanism, which improves the traceability of generated research ideas. It further facilitates evidence-grounded manuscript drafting guided by structured medical compositional conventions and ethical policies. The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration, each corresponding to a distinct level of automated scientific inquiry with progressively increasing autonomy. Comprehensive evaluations by both large language models and human experts demonstrate that the ideas generated by the Medical AI Scientist are of substantially higher quality than those produced by commercial LLMs across 171 cases, 19 clinical tasks, and 6 data modalities. Meanwhile, our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments. Double-blind evaluations by human experts and the Stanford Agentic Reviewer suggest that the generated manuscripts approach MICCAI-level quality, while consistently surpassing those from ISBI and BIBM. The proposed Medical AI Scientist highlights the potential of leveraging AI for autonomous scientific discovery in healthcare.
69cb2ff522f9e9dce3fe1f09
Hongtao Wu
claimed_verified
2026-03-31T09:28:43.815Z
66f287a7aef962b237a95d94
/avatars/920b84799f819dc535d922290159c2e8.svg
HunterWu
HunterWu
user
HunterWu
69cb2ff522f9e9dce3fe1f0a
Boyun Zheng
admin_assigned
2026-03-31T10:17:25.704Z
670f44a090379f8b59b4dfd5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/EYOe61xYqQhptStWRB9ZB.png
Boyun Zheng
Byzzz0301
user
Byzzz0301
69cb2ff522f9e9dce3fe1f0b
Dingjie Song
claimed_verified
2026-03-31T09:28:45.922Z
619f01b8cc04eadf54fa5d5d
/avatars/928f3d1a6146e2e1ae4860445d929d5c.svg
Song Dingjie
songdj
user
songdj
69cb2ff522f9e9dce3fe1f0c
Yu Jiang
69cb2ff522f9e9dce3fe1f0d
Jianfeng Gao
admin_assigned
2026-03-31T09:35:11.875Z
641904caf9d6f1d772ec7af7
/avatars/4a63eac71eb30f70b1a0e9d4708f26c1.svg
Jianfeng Gao
wyngjf
user
wyngjf
69cb2ff522f9e9dce3fe1f0e
Lei Xing
69cb2ff522f9e9dce3fe1f0f
Lichao Sun
admin_assigned
2026-03-31T09:35:05.852Z
65a52766215aabac489e3468
/avatars/fe05e22cd7e12e961296426434e17c76.svg
Lichao Sun
sunlichao137
user
sunlichao137
69cb2ff522f9e9dce3fe1f10
Yixuan Yuan
admin_assigned
2026-03-31T09:34:59.757Z
640fdfc9f2d7c41a1ea112ef
/avatars/780328c388ac4bc9acdf063e7833259d.svg
yxyuan
yixuanyuan
user
yixuanyuan
Towards a Medical AI Scientist
Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this work, we introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research. It enables clinically grounded ideation by transforming extensively surveyed literature into actionable evidence through clinician-engineer co-reasoning mechanism, which improves the traceability of generated research ideas. It further facilitates evidence-grounded manuscript drafting guided by structured medical compositional conventions and ethical policies. The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration, each corresponding to a distinct level of automated scientific inquiry with progressively increasing autonomy. Comprehensive evaluations by both large language models and human experts demonstrate that the ideas generated by the Medical AI Scientist are of substantially higher quality than those produced by commercial LLMs across 171 cases, 19 clinical tasks, and 6 data modalities. Meanwhile, our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments. Double-blind evaluations by human experts and the Stanford Agentic Reviewer suggest that the generated manuscripts approach MICCAI-level quality, while consistently surpassing those from ISBI and BIBM. The proposed Medical AI Scientist highlights the potential of leveraging AI for autonomous scientific discovery in healthcare.
Medical AI Scientist represents the first autonomous research framework designed for clinical applications, enabling evidence-based hypothesis generation and manuscript drafting through clinician-engineer collaboration across three research modes.
autonomous research framework clinical autonomous research clinician-engineer co-reasoning mechanism evidence-grounded manuscript drafting structured medical compositional conventions ethical policies paper-based reproduction literature-inspired innovation task-driven exploration large language models human experts executable experiments MICCAI-level quality ISBI BIBM