NDLI: 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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Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do

Content Provider	Semantic Scholar
Author	Park, Seong Ho Kressel, Herbert Y.
Copyright Year	2018
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.
Starting Page	2113
Ending Page	2131
Page Count	19
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.jkms.org/Synapse/Data/PDFData/0063JKMS/jkms-33-e152.pdf
PubMed reference number	5966371
Alternate Webpage(s)	https://doi.org/10.3346/jkms.2018.33.e152
DOI	10.3346/jkms.2018.33.e152
Journal	Journal of Korean medical science
Volume Number	33
Language	English
Access Restriction	Open
Subject Keyword	Artificial Intelligence Journal NCI Unconventional Innovations Program Patients Projections and Predictions Technological Innovations
Content Type	Text
Resource Type	Article

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