Ethical Implications of AI in Healthcare
Finally, there is also a variety of ethical implications around the use of AI in healthcare. Healthcare decisions have been made almost exclusively by humans in the past, and the use of smart machines to make or assist with them raises issues of accountability, transparency, permission, and privacy.
Perhaps, the most difficult issue to address given today’s technologies is transparency. I mentioned above that AI algorithms—particularly deep learning algorithms used for image analysis—are virtually impossible to interpret or explain. If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation.
Mistakes will undoubtedly be made by AI systems in patient diagnosis and treatment, and it may be difficult to establish accountability for them. There are also likely to be incidents in which patients receive medical information from AI systems that they would prefer to receive from an empathetic clinician. Machine learning systems in healthcare may also be subject to algorithmic bias, perhaps predicting greater likelihood of disease on the basis of gender or race when those are not actually causal factors.44
We are likely to eventually encounter many ethical, medical, occupational, and technological changes with AI in healthcare. It is important that healthcare institutions, as well as governmental and regulatory bodies, establish monitoring and governance structures to monitor key issues, react in a responsible manner, and establish governance mechanisms to limit negative implications. AI is one of the more powerful and consequential technologies to impact human societies, so it will require continuous attention and thoughtful policy for many years.
The Future of AI in Healthcare
There can be little doubt that AI has an important role to play in the healthcare offerings of the future. In the form of machine learning, it is the primary capability behind the development of precision medicine—widely agreed to be a sorely needed advance in care. AI has already proven valuable for administrative applications, and shows promise for engagement and adherence purposes. Although early efforts at providing diagnosis and treatment recommendations have proven challenging, it seems likely that AI will ultimately make significant contributions to that domain. Given the rapid advances in AI for imaging analysis, it seems likely that most radiology and pathology images will be examined at some point by a machine. Speech and text recognition are already employed for tasks such as patient communication and capture of clinical notes, but their usage will increase.
The greatest challenge to AI in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice. For widespread adoption to take place, AI systems must be approved by regulators, integrated with EHR systems, standardized to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organizations, and updated over time in the field. These challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature. As a result, we expect to see limited use of AI in clinical practice within five years, and more extensive use within ten.
It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients. Over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills such as empathy, persuasion, and big-picture care integration. Perhaps, the only healthcare providers who will lose their jobs over time may be those who refuse to work alongside AI.
This chapter is a revised and extended version of Davenport, T.H. and Kalakota, R., “The Potential for Artificial Intelligence in Healthcare,” Future Healthcare Journal, June 2019, DOI: 10.7861/futurehosp.6-2-94
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