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Review
. 2021 Oct;191(10):1673-1683.
doi: 10.1016/j.ajpath.2021.06.011. Epub 2021 Jul 10.

Ethics of AI in Pathology: Current Paradigms and Emerging Issues

Chhavi Chauhan  1 , Rama R Gullapalli  2
Affiliations
Review

Ethics of AI in Pathology: Current Paradigms and Emerging Issues

Chhavi Chauhan et al. Am J Pathol. 2021 Oct.

Abstract

Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of pathology.

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Figures

Figure 1
Figure 1
Key participants and issues of emerging importance in the study of artificial intelligence (AI) ethics in pathology. Understanding the ethical issues shown at the center of the image is critical to enable a robust framework of AI implementation within pathology practice. These topics are common across various fields of study and also unique within the context of each specialty. Interactions between the various participants (patients, AI researchers, pathologists, and institutions) are central to developing a comprehensive framework of understanding AI ethics in pathology.
See this image and copyright information in PMC

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