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    <abstract>Large language models (LLMs) are increasingly used in agentic systems that reason over multiple steps, call tools, and interact with clinical knowledge sources. This survey examines whether LLM-based medical agents approximate expert diagnostic reasoning or reproduce its outputs without its causal and epistemic foundations. We organize agent design along five axes: reasoning paradigms, agent architectures, tool use, degree of autonomy, and embodiment. We then analyze learning and adaptation, evaluation, safety, and deployment as cross-cutting concerns that interact with these axes. Drawing on research in machine learning, biomedical informatics, and clinical AI, we examine how design choices shape diagnostic reliability and failure modes, and identify requirements for clinical and medical agents.</abstract>
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%0 Conference Proceedings
%T Is There a Dr. House in the Machine? Towards Clinical and Diagnostic Reasoning in LLM-based Medical Agents
%A Wasi, Azmine Toushik
%A Rahman, Abdur
%A Islam, Riashat
%A Parvez, Md Rizwan
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F wasi-etal-2026-dr
%X Large language models (LLMs) are increasingly used in agentic systems that reason over multiple steps, call tools, and interact with clinical knowledge sources. This survey examines whether LLM-based medical agents approximate expert diagnostic reasoning or reproduce its outputs without its causal and epistemic foundations. We organize agent design along five axes: reasoning paradigms, agent architectures, tool use, degree of autonomy, and embodiment. We then analyze learning and adaptation, evaluation, safety, and deployment as cross-cutting concerns that interact with these axes. Drawing on research in machine learning, biomedical informatics, and clinical AI, we examine how design choices shape diagnostic reliability and failure modes, and identify requirements for clinical and medical agents.
%U https://aclanthology.org/2026.findings-aacl.82/
%P 1632-1667

Markdown (Informal)

[Is There a Dr. House in the Machine? Towards Clinical and Diagnostic Reasoning in LLM-based Medical Agents](https://aclanthology.org/2026.findings-aacl.82/) (Wasi et al., Findings 2026)

• Is There a Dr. House in the Machine? Towards Clinical and Diagnostic Reasoning in LLM-based Medical Agents (Wasi et al., Findings 2026)

ACL

• Azmine Toushik Wasi, Abdur Rahman, Riashat Islam, and Md Rizwan Parvez. 2026. Is There a Dr. House in the Machine? Towards Clinical and Diagnostic Reasoning in LLM-based Medical Agents. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 1632–1667, Hengqin, China. Association for Computational Linguistics.