Opening the black box of AI‐Medicine

One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a “black box.” Machine Learning (ML) can assist in reading radiological, endoscopic and histological pic...

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Vydané v:Journal of gastroenterology and hepatology Ročník 36; číslo 3; s. 581 - 584
Hlavní autori: Poon, Aaron I F, Sung, Joseph J Y
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Australia Wiley Subscription Services, Inc 01.03.2021
Predmet:
Algorithms
Artificial intelligence
black box
Decision making
gastroenterology
Human error
Learning algorithms
Machine learning
Medicine
medicine
black box
gastroenterology
ISSN:0815-9319, 1440-1746, 1440-1746
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Popis
Shrnutí:One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a “black box.” Machine Learning (ML) can assist in reading radiological, endoscopic and histological pictures, suggesting diagnosis and predict disease outcome, and even recommending therapy and surgical decisions. However, clinical adoption of these AI tools has been slow because of a lack of trust. Besides clinician's doubt, patients lacking confidence with AI‐powered technologies also hamper development. While they may accept the reality that human errors can occur, little tolerance of machine error is anticipated. In order to implement AI medicine successfully, interpretability of ML algorithm needs to improve. Opening the black box in AI medicine needs to take a stepwise approach. Small steps of biological explanation and clinical experience in ML algorithm can help to build trust and acceptance. AI software developers will have to clearly demonstrate that when the ML technologies are integrated into the clinical decision‐making process, they can actually help to improve clinical outcome. Enhancing interpretability of ML algorithm is a crucial step in adopting AI in medicine.
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ISSN:0815-9319
1440-1746
1440-1746
DOI:10.1111/jgh.15384