How generative artificial intelligence can facilitate the teaching of clinical reasoning: a scoping review

Guilherme Freitas Bernardo Ferreira; Alexandre Sampaio  Moura; Lígia Maria Cayres  Ribeiro; Maria Aparecida  Turci; Sílvia Mamede

doi:10.35699/2237-5864.2025.58339

Authors

Guilherme Freitas Bernardo Ferreira Universidade Professor Edson Antônio Velano https://orcid.org/0009-0006-2383-7462
Alexandre Sampaio Moura Faculdade Santa Casa https://orcid.org/0000-0002-4818-5425
Lígia Maria Cayres Ribeiro University Medical Center Groningen https://orcid.org/0000-0002-4277-3066
Maria Aparecida Turci Universidade Professor Edson Antonio Velano https://orcid.org/0000-0002-4380-4231
Sílvia Mamede University Medical Center Groningen https://orcid.org/0000-0003-1187-2392

DOI:

https://doi.org/10.35699/2237-5864.2025.58339

Keywords:

artificial intelligence, clinical reasoning, generative artificial intelligence, health education, scoping review

Abstract

This review aims to map and summarize the current state of research to identify the applicability of chatbots in teaching clinical reasoning during medical training, considering the best available evidence. A systematic and comprehensive search was conducted in PubMed/MEDLINE, Web of Science, and Google Scholar databases between August 2023 and August 2024. Original studies describing educational applications aligned with evidence-based strategies for teaching clinical reasoning (self-explanation, structured reflection, case practice, and feedback) were included. The selection was complemented by snowballing and expert consultation. Twenty-one publications were included. All studies explored the use of ChatGPT (OpenAI); three (14%) also analyzed Bard (Google), two (9.5%) investigated Bing (Microsoft), and one (5%) explored other artificial intelligence tools. Our findings suggest that chatbots can support the development of clinical reasoning skills through effective educational strategies. Chatbot responses can help students build understanding, promote deliberate reflection, encourage feedback when practicing with written cases, and adapt content to the learner’s stage. Few studies raised concerns about risks and ethical issues. This review demonstrated that chatbots hold great potential to enhance the development of clinical reasoning during medical education. However, it is essential to address inherent limitations, such as the risks of hallucinations and inaccurate explanations, to maximize the technology’s educational potential.

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Author Biographies

Guilherme Freitas Bernardo Ferreira, Universidade Professor Edson Antônio Velano

Guilherme Freitas Bernardo Ferreira is a neurologist with a master’s degree in Health Education/Clinical Reasoning Education. He served as a professor at Unifenas-BH from 2021 to 2025.
Alexandre Sampaio Moura, Faculdade Santa Casa

Alexandre Sampaio Moura is an infectious diseases physician working as a full professor at the Graduate Program in Medicine and Biomedicine at Faculdade Santa Casa, Belo Horizonte, Brazil. Prof. Moura conducts research in medical education, with a particular interest in clinical reasoning and competence-based assessment.
Lígia Maria Cayres Ribeiro, University Medical Center Groningen

Ligia Cayres Ribeiro is an internal medicine physician with a PhD in Clinical Reasoning. She is a researcher at the University Medical Center Groningen, where she investigates how technology can enhance evidence-informed educational practices.
Maria Aparecida Turci, Universidade Professor Edson Antonio Velano

Maria Aparecida Turci is a public health professional working as a full professor at the Graduate Program in Medicine at Professor Edson Antônio Velano University, Belo Horizonte, Brazil. Prof. Turci conducts research in public health and health professions education.
Sílvia Mamede, University Medical Center Groningen

Sílvia Mamede is a guest professor at the Wenckebach Institute (WIOO), Lifelong Learning, Education and Assessment Research Network (LEARN), University Medical Center Groningen, Netherlands. She conducts research on clinical reasoning and diagnostic error in medicine; educational strategies for the teaching of clinical reasoning; reflection and experiential learning in medical education and clinical practice.

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