The Ethics of Machine Learning in Medical Sciences: Where Do We Stand Today?

被引:13
作者
Basu, Treena [1 ]
Engel-Wolf, Sebastian [4 ]
Menzer, Olaf [2 ,3 ]
机构
[1] Occidental Coll, Dept Math, 1600 Campus Rd, Los Angeles, CA 90041 USA
[2] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA
[3] Pacific Life, Technol Dept, Retirement Solut Div, Newport Beach, CA USA
[4] Tech Univ Munich, Syst Biotechnol Grp, Boltzmannstr 15, Garching, Germany
关键词
Best practices; ethics; electronic health records; machine learning; CLASSIFICATION;
D O I
10.4103/ijd.IJD_419_20
中图分类号
R75 [皮肤病学与性病学];
学科分类号
100206 ;
摘要
Advances in Machine Learning and availability of state-of-the-art computational resources, along with digitized healthcare data, have set the stage for extensive application of artificial intelligence in the realm of diagnosis, prognosis, clinical decision support, personalized treatment options, drug development, and the field of biomedicine. Here, we discuss the application of Machine Learning algorithms in patient healthcare and dermatological domains along with the ethical complexities that are involved. In scientific studies, ethical challenges were initially not addressed proportionally (as assessed by keyword counts in PubMed) and just more recently (since 2016) this has started to improve. Few pioneering countries have created regulatory guidelines around how to respect matters of (1) privacy, (2) fairness, (3) accountability, (4) transparency and (5) conflict of interest when developing novel medical Machine Learning applications. While there is a strong promise of emerging medical applications to ultimately benefit both the patients and the medical practitioners, it is important to raise awareness on the five key ethical issues and incorporate them into medical practice in the near future.
引用
收藏
页码:358 / 364
页数:7
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