J Korean Med Sci.  2018 May;33(22):e152. 10.3346/jkms.2018.33.e152.

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

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. parksh.radiology@gmail.com
  • 2Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

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.

Keyword

Artificial Intelligence; Machine Learning; Decision Support Techniques; Peer Review; Journalism, Medical; Validation Studies

MeSH Terms

Artificial Intelligence*
Decision Support Techniques
Diagnosis
Humans
Inventions*
Journalism, Medical
Machine Learning
Patient Safety
Peer Review
Research Personnel
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