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Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review.
Lam, Thomas Y T; Cheung, Max F K; Munro, Yasmin L; Lim, Kong Meng; Shung, Dennis; Sung, Joseph J Y.
Afiliação
  • Lam TYT; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
  • Cheung MFK; Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong., Hong Kong, Hong Kong.
  • Munro YL; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
  • Lim KM; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
  • Shung D; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
  • Sung JJY; Department of Medicine (Digestive Diseases), Yale School of Medicine, New Haven, CT, United States.
J Med Internet Res ; 24(8): e37188, 2022 08 25.
Article em En | MEDLINE | ID: mdl-35904087
BACKGROUND: The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care. OBJECTIVE: This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice. METHODS: CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. RESULTS: Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies. CONCLUSIONS: There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine. TRIAL REGISTRATION: PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial Tipo de estudo: Clinical_trials / Guideline / Systematic_reviews Limite: Humans País/Região como assunto: America do norte / Europa Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Hong Kong

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial Tipo de estudo: Clinical_trials / Guideline / Systematic_reviews Limite: Humans País/Região como assunto: America do norte / Europa Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Hong Kong