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The Role of Artificial Intelligence in Medical Education: A Systematic Review.
Tozsin, Atinc; Ucmak, Harun; Soyturk, Selim; Aydin, Abdullatif; Gozen, Ali Serdar; Fahim, Maha Al; Güven, Selcuk; Ahmed, Kamran.
Afiliação
  • Tozsin A; Department of Urology, Trakya University School of Medicine, Edirne, Turkey.
  • Ucmak H; Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey.
  • Soyturk S; Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey.
  • Aydin A; MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK.
  • Gozen AS; Department of Urology, King's College Hospital NHS Foundation Trust, London, UK.
  • Fahim MA; Department of Urology, Medius Kliniken, Ostfildern, Germany.
  • Güven S; Medical Education Department, Sheikh Khalifa Medical City, Abu Dhabi, UAE.
  • Ahmed K; Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey.
Surg Innov ; : 15533506241248239, 2024 Apr 17.
Article em En | MEDLINE | ID: mdl-38632898
ABSTRACT

BACKGROUND:

To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study.

METHODS:

This review (PROSPERO ID CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found.

RESULTS:

After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%.

CONCLUSION:

AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article