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Generative artificial intelligence in clinical reasoning and differential diagnosis in internal medicine.

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Τίτλος: Generative artificial intelligence in clinical reasoning and differential diagnosis in internal medicine.
Μεταγλωττισμένος Τίτλος: Inteligencia artificial generativa para el razonamiento clínico y el diagnóstico diferencial en medicina interna.
Συγγραφείς: Corral-Gudino L; Departamento de Medicina, Dermatología y Toxicología, Facultad de Medicina, Universidad de Valladolid. Servicio de Medicina Interna, Hospital Universitario Río Hortega, Valladolid, España. Electronic address: luis.corral@uva.es., Ramos-Casals M; Departamento de Medicina, Universidad de Barcelona, Barcelona, España. Servicio de Enfermedades Autoinmunes, ICMiD, Hospital Clínic, Barcelona, España., Marcos M; Departamento de Medicina, Universidad de Salamanca, Salamanca, España. Servicio de Medicina Interna, Hospital Universitario de Salamanca, Salamanca, España., Ríos-Blanco JJ; Departamento de Medicina, Universidad Autónoma de Madrid, Madrid, España. Servicio de Medicina Interna, Hospital Universitario La Paz, Madrid, España.
Πηγή: Medicina clinica [Med Clin (Barc)] 2026 Oct; Vol. 166 (10), pp. 107567. Date of Electronic Publication: 2026 Aug 21.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English; Spanish; Castilian
Στοιχεία περιοδικού: Publisher: Elsevier España Country of Publication: Spain NLM ID: 0376377 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1578-8989 (Electronic) Linking ISSN: 00257753 NLM ISO Abbreviation: Med Clin (Barc) Subsets: MEDLINE
Imprint Name(s): Publication: <2009- > : Madrid : Elsevier España
Original Publication: Barcelona : Ediciones Doyma, S.A.
Ιατρικοί όροι (MeSH): Internal Medicine*/methods , Generative Artificial Intelligence* , Clinical Reasoning* , Artificial Intelligence*, Humans ; Diagnosis, Differential ; Large Language Models ; Clinical Competence
Περίληψη: Clinical reasoning and differential diagnosis are core competencies in medicine. Large language models (LLMs) have generated considerable interest as potential tools to support these skills. This article presents a narrative review of the available evidence, organized around five key questions: the effect of LLMs on diagnostic reasoning, the optimal design of clinician-LLM interaction, the appropriate timing of consultation during the clinical encounter, the safest models of clinical-AI integration, and the main risks associated with their use. The evidence shows that LLMs improve differential diagnosis when used by trained professionals within structured workflows. However, passive use generates biases, and clinician-AI collaboration may not consistently outperform autonomous LLMs. A practical framework stratified by degree of diagnostic uncertainty is proposed, with operational and educational recommendations oriented toward "physician-in-the-loop" models, in which LLMs amplify, challenge, and make explicit the diagnostic reasoning process under critical human oversight.
(Copyright © 2026 The Authors. Published by Elsevier España, S.L.U. All rights reserved.)
Contributed Indexing: Keywords: Clinical Competence; Clinical Decision-Making; Clinical Reasoning; Competencia clínica; Diagnosis Differential; Diagnóstico diferencial; Generative Artificial Intelligence; Inteligencia artificial generativa; Razonamiento clínico; Toma de decisiones clínicas
Entry Date(s): Date Created: 20260821 Date Completed: 20261003 Latest Revision: 20261003
Update Code: 20261004
DOI: 10.1016/j.medcli.2026.107567
PMID: 42628288
Βάση Δεδομένων: MEDLINE
FullText Text:
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  Data: Generative artificial intelligence in clinical reasoning and differential diagnosis in internal medicine.
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  Data: &lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Corral-Gudino+L%22&quot;&gt;Corral-Gudino L&lt;/searchLink&gt;; Departamento de Medicina, Dermatolog&#237;a y Toxicolog&#237;a, Facultad de Medicina, Universidad de Valladolid. Servicio de Medicina Interna, Hospital Universitario R&#237;o Hortega, Valladolid, Espa&#241;a. Electronic address: luis.corral@uva.es.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Ramos-Casals+M%22&quot;&gt;Ramos-Casals M&lt;/searchLink&gt;; Departamento de Medicina, Universidad de Barcelona, Barcelona, Espa&#241;a. Servicio de Enfermedades Autoinmunes, ICMiD, Hospital Cl&#237;nic, Barcelona, Espa&#241;a.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Marcos+M%22&quot;&gt;Marcos M&lt;/searchLink&gt;; Departamento de Medicina, Universidad de Salamanca, Salamanca, Espa&#241;a. Servicio de Medicina Interna, Hospital Universitario de Salamanca, Salamanca, Espa&#241;a.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22R&#237;os-Blanco+JJ%22&quot;&gt;R&#237;os-Blanco JJ&lt;/searchLink&gt;; Departamento de Medicina, Universidad Aut&#243;noma de Madrid, Madrid, Espa&#241;a. Servicio de Medicina Interna, Hospital Universitario La Paz, Madrid, Espa&#241;a.
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  Data: Clinical reasoning and differential diagnosis are core competencies in medicine. Large language models (LLMs) have generated considerable interest as potential tools to support these skills. This article presents a narrative review of the available evidence, organized around five key questions: the effect of LLMs on diagnostic reasoning, the optimal design of clinician-LLM interaction, the appropriate timing of consultation during the clinical encounter, the safest models of clinical-AI integration, and the main risks associated with their use. The evidence shows that LLMs improve differential diagnosis when used by trained professionals within structured workflows. However, passive use generates biases, and clinician-AI collaboration may not consistently outperform autonomous LLMs. A practical framework stratified by degree of diagnostic uncertainty is proposed, with operational and educational recommendations oriented toward &quot;physician-in-the-loop&quot; models, in which LLMs amplify, challenge, and make explicit the diagnostic reasoning process under critical human oversight.&lt;br /&gt; (Copyright &#169; 2026 The Authors. Published by Elsevier Espa&#241;a, S.L.U. All rights reserved.)
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