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<p>Conventional artificial intelligence has achieved remarkable feats in identifying associations and predictive patterns, yet its limitations in causal reasoning present a hurdle for complex clinical challenges demanding deep professional expertise. Large reasoning models are creating opportunities to move beyond correlation towards emulating the analytical processes of humans. Applied to the practice of medicine, medical reasoning artificial intelligence (MRAI) envisions systems that can engage directly in patient care, draw on diverse clinical data and decision-support tools, and refine their reasoning by learning from clinician feedback and patient outcomes. Unlike traditional models that operate within fixed parameters, MRAI is expected to redefine clinical artificial intelligence as a thinking partner, enabling a more nuanced understanding of complex medical scenarios. We anticipate that MRAI may act as a collaborative aide, augmenting connection and decisions by managing complex evidence. This paradigm shift is expected to profoundly extend our understanding of medicine, free clinicians for more direct patient care, offer clearer insights and accelerate discovery.</p>

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Large reasoning models as thinking machines for medicine

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Hong-Yu Zhou,
  • Adam Rodman,
  • Peng Liu,
  • Pranav Rajpurkar,
  • Tony Y Hu,
  • Tien Yin Wong,
  • Eric J. Topol

摘要

Conventional artificial intelligence has achieved remarkable feats in identifying associations and predictive patterns, yet its limitations in causal reasoning present a hurdle for complex clinical challenges demanding deep professional expertise. Large reasoning models are creating opportunities to move beyond correlation towards emulating the analytical processes of humans. Applied to the practice of medicine, medical reasoning artificial intelligence (MRAI) envisions systems that can engage directly in patient care, draw on diverse clinical data and decision-support tools, and refine their reasoning by learning from clinician feedback and patient outcomes. Unlike traditional models that operate within fixed parameters, MRAI is expected to redefine clinical artificial intelligence as a thinking partner, enabling a more nuanced understanding of complex medical scenarios. We anticipate that MRAI may act as a collaborative aide, augmenting connection and decisions by managing complex evidence. This paradigm shift is expected to profoundly extend our understanding of medicine, free clinicians for more direct patient care, offer clearer insights and accelerate discovery.