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Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.
Han, Ryan; Acosta, Julián N; Shakeri, Zahra; Ioannidis, John P A; Topol, Eric J; Rajpurkar, Pranav.
Afiliação
  • Han R; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; University of California Los Angeles-Caltech Medical Scientist Training Program, Los Angeles, CA, USA.
  • Acosta JN; Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Rad AI, San Francisco, CA, USA.
  • Shakeri Z; Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
  • Ioannidis JPA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA.
  • Topol EJ; Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA. Electronic address: etopol@scripps.edu.
  • Rajpurkar P; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Lancet Digit Health ; 6(5): e367-e373, 2024 May.
Article em En | MEDLINE | ID: mdl-38670745
ABSTRACT
This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Ensaios Clínicos Controlados Aleatórios como Assunto Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Ensaios Clínicos Controlados Aleatórios como Assunto Idioma: En Ano de publicação: 2024 Tipo de documento: Article