Patterns· 2021Q1
Addressing bias in big data and AI for health care: A call for open science
- 810citations
- Q1SCImago
- 2021year
Short summary
AI algorithms in health care risk reinforcing bias due to underrepresentation of certain populations in training data, potentially leading to misdiagnoses and fatal outcomes.
AI-generated from the title and abstract; the full text is not read.
Key points
- AI in health care is prone to algorithmic bias due to unrepresentative training data.
- Underrepresented groups in biomedical datasets lead to AI reinforcing societal biases.
- Biased AI can result in misdiagnoses, fatal outcomes, and lack of generalization.
- Open science tools are proposed as concrete steps to address AI bias in health care.
AI-generated from the title and abstract; the full text is not read.
Abstract
Artificial intelligence (AI) has an astonishing potential in assisting clinical decision making and revolutionizing the field of health care. A major open challenge that AI will need to address before its integration in the clinical routine is that of algorithmic bias. Most AI algorithms need big datasets to learn from, but several groups of the human population have a long history of being absent or misrepresented in existing biomedical datasets. If the training data is misrepresentative of the population variability, AI is prone to reinforcing bias, which can lead to fatal outcomes, misdiagnoses, and lack of generalization. Here, we describe the challenges in rendering AI algorithms fairer, and we propose concrete steps for addressing bias using tools from the field of open science.
The authors' abstract, as published at the source. Patterns, 2021 · DOI ↗
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Field: Health Informatics
Health InformaticsMedicine