Publication: Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do
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Date
2018
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Published Version
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Volume Title
Publisher
The Korean Academy of Medical Sciences
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Citation
Park, Seong Ho, and Herbert Y. Kressel. 2018. “Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do.” Journal of Korean Medical Science 33 (22): e152. doi:10.3346/jkms.2018.33.e152. http://dx.doi.org/10.3346/jkms.2018.33.e152.
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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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Keywords
Editing, Writing & Publishing, Artificial Intelligence, Machine Learning, Decision Support Techniques, Peer Review, Journalism, Medical, Validation Studies
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