Evidence map›Paper›PMID 42234872›Full record

ArticleJMIR formative research2026

Clinical Evaluation of the Clinical Reasoning Process of Large Language Models in Nephrology: Comparative Evaluation Study.

Yuichiro Yano, Hiroaki Kakizaki, Hajime Nagasu, Seiji Kishi, Takeo Koshida, Yoshihito Nihei, Akira Hirano, Masaomi Nangaku, Hirotake Mori, Toshio Naito and 7 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Yuichiro YanoDepartment of General Medicine, Juntendo University, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.ORCID 0000-0002-2565-7290
Hiroaki KakizakiPeopleMedia, Inc, Osaka, Japan.ORCID 0009-0004-1616-4744
Hajime NagasuDepartment of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.ORCID 0000-0003-2549-4126
Seiji KishiDepartment of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.ORCID 0000-0001-6205-1661
Takeo KoshidaDepartment of Nephrology, Juntendo University, Tokyo, Japan.ORCID 0009-0007-0152-3443
Yoshihito NiheiDepartment of Nephrology, Juntendo University, Tokyo, Japan.ORCID 0000-0002-0345-0192
Akira HiranoDepartment of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.ORCID 0009-0007-4576-998X
Masaomi NangakuDivision of Nephrology and Endocrinology, The University of Tokyo, Tokyo, Japan.ORCID 0000-0001-7401-2934
Hirotake MoriDepartment of General Medicine, Juntendo University, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.ORCID 0000-0002-7988-5758
Toshio NaitoDepartment of General Medicine, Juntendo University, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.ORCID 0000-0003-1646-9930
Mizuki OhashiDepartment of General Medicine, Juntendo University, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan, 81 3-3813-3111.ORCID 0000-0001-7770-5656
Shoichi MaruyamaDepartment of Nephrology, Nagoya University, Nagoya, Aichi, Japan.ORCID 0000-0002-8858-632X
Isao MatsuiDepartment of Nephrology, The University of Osaka, Suita, Osaka, Japan.ORCID 0000-0001-8378-6130
Yoshitaka IsakaDepartment of Nephrology, The University of Osaka, Suita, Osaka, Japan.ORCID 0000-0002-0820-7167
Hirokazu OkadaDepartment of Nephrology, Saitama Medical University, Saitama, Japan.ORCID 0000-0002-1839-9078
Yusuke SuzukiDepartment of Nephrology, Juntendo University, Tokyo, Japan.ORCID 0000-0002-4077-9747
Naoki KashiharaDepartment of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Okayama, Japan.ORCID 0000-0002-9487-984X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: This study evaluates the dynamic clinical reasoning of 4 leading large language models in complex nephrology cases, demonstrating that while Gemini 2.5 Pro achieved the highest reasoning scores and computational efficiency, all tested models excelled at static data synthesis but shared vulnerabilities in formulating nuanced differential diagnoses and in prospective clinical planning.

Indexed as

Clinical ReasoningLarge Language ModelsNephrologyHumansartificial intelligenceclinical reasoningevaluationlarge language modelsnephrology

Identifiers

PMID42234872
PMCPMC13232913

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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.