ReviewNature biomedical engineering2026
Large reasoning models as thinking machines for medicine.
Review in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
42337060What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.