ArticleNature communications2026
Grounding large language models in clinical diagnostics.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Letter to Editor Concerning: Evaluating AI Accuracy in the Diagnosis and Treatment of Disorders of Gut-Brain Interaction.Neurogastroenterology and motility · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although Large Language Models (LLMs) possess extensive medical knowledge, they often struggle to emulate the complex, iterative process of real-world clinical diagnosis. To address this limitation, we present ClinDiag-GPT, a specialized LLM fine-tuned to execute full diagnostic procedures, supported by the ClinDiag-Framework evaluation system and ClinDiag-Benchmark, a dataset comprising 4,421 real-world cases. Our evaluation shows that existing LLMs, including GPT-4o-mini, GPT-4o, Claude-3-Haiku, Qwen2.5-72b, Qwen2.5-32b, and Qwen2.5-14b, while proficient in static tasks, fall short in dynamic diagnostic workflows and frequently commit clinical errors. In contrast, ClinDiag-GPT, trained on clinical cases, outperforms all baseline models in both diagnostic accuracy and procedural performance. Furthermore, a comparative analysis reveals that collaboration between physicians and ClinDiag-GPT yields higher diagnostic accuracy and efficiency compared to either working alone, demonstrating the utility of ClinDiag-GPT as a clinical assistant.
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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.