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Science Editing > Epub ahead of print
Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review
Seong Ho Park1 , Young-Hak Kim2 , Jun Young Lee3 , Soyoung Yoo4 , Chong Jai Kim5
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
2Cardiology Division, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
3National IT Industry Promotion Agency, Jincheon, Korea
4Health Innovation Big Data Center, Asan Medical Center, Seoul, Korea
5Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
Corresponding Author: Seong Ho Park ,Email: seongho@amc.seoul.kr
Received: May 16, 2019;  Accepted: May 28, 2019.
ABSTRACT
This review article aims to highlight several areas in research studies on artificial intelligence (AI) in medicine that currently require additional transparency and explain why additional transparency is needed. Transparency regarding training data, test data and results, interpretation of study results, and the sharing of algorithms and data are major areas for guaranteeing ethical standards in AI research. For transparency in training data, clarifying the biases and errors in training data and the AI algorithms based on these training data prior to their implementation is critical. Furthermore, biases about institutions and socioeconomic groups should be considered. For transparency in test data and test results, authors should state if the test data were collected externally or internally and prospectively or retrospectively at first. It is necessary to distinguish whether datasets were convenience samples consisting of some positive and some negative cases or clinical cohorts. When datasets from multiple institutions were used, authors should report results from each individual institution. Full publication of the results of AI research is also important. For transparency in interpreting study results, authors should interpret the results explicitly and avoid over-interpretation. For transparency by sharing algorithms and data, sharing is required for replication and reproducibility of the research by other researchers. All of the above mentioned high standards regarding transparency of AI research in healthcare should be considered to facilitate the ethical conduct of AI research.
Keywords: Artificial intelligence; Ethics; Research; Publishing; Bias
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