In The American journal of pathology ; h5-index 54.0
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, impacting many traditional fields of medicine. Pathology 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 healthcare delivery. Equally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the healthcare delivery process. Unethical use of AI may exacerbate existing inequities of healthcare, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. In this mini-review, we explore 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. We describe three key foundational principles of ethical AI in the context of pathology: transparency, accountability, and governance. The future practice of pathology must be guided by these principles. Pathologists must be aware of the potential of AI to deliver superlative healthcare and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive the future implementation of ethical AI in the practice of pathology.
Chauhan Chhavi, Gullapalli Rama R
Algorithmic Decision-Making, Artificial Intelligence, Big Data, Deep Learning, Digital Pathology, Ethics, Privacy, Risk