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Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do
Article em En | WPRIM | ID: wpr-714575
Biblioteca responsável: WPRO
ABSTRACT
Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies and, as a result, very few are currently in clinical use. A thorough, systematic validation of AI technologies using adequately designed clinical research studies before their integration into clinical practice is critical to ensure patient benefit and safety while avoiding any inadvertent harms. We would like to suggest several specific points regarding the role that peer-reviewed medical journals can play, in terms of study design, registration, and reporting, to help achieve proper and meaningful clinical validation of AI technologies designed to make medical diagnosis and prediction, focusing on the evaluation of diagnostic accuracy efficacy. Peer-reviewed medical journals can encourage investigators who wish to validate the performance of AI systems for medical diagnosis and prediction to pay closer attention to the factors listed in this article by emphasizing their importance. Thereby, peer-reviewed medical journals can ultimately facilitate translating the technological innovations into real-world practice while securing patient safety and benefit.
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Texto completo: 1 Índice: WPRIM Assunto principal: Revisão por Pares / Pesquisadores / Tradução / Inteligência Artificial / Técnicas de Apoio para a Decisão / Jornalismo Médico / Diagnóstico / Segurança do Paciente / Invenções / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Journal of Korean Medical Science Ano de publicação: 2018 Tipo de documento: Article
Texto completo: 1 Índice: WPRIM Assunto principal: Revisão por Pares / Pesquisadores / Tradução / Inteligência Artificial / Técnicas de Apoio para a Decisão / Jornalismo Médico / Diagnóstico / Segurança do Paciente / Invenções / Aprendizado de Máquina Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Journal of Korean Medical Science Ano de publicação: 2018 Tipo de documento: Article