The role of artificial intelligence, or machine learning, has become more and more important as the technology industry wrestles with large amounts of data that may improve- or confuse- health cost priorities, according to a National Academy of Medicine Special Publication on the use of AI in healthcare. Yet, the current explosion of investments and innovations is developing without implementing responsible, and transparent deployment, which limits their potential.
"It's critical for the health care community to learn from both the successes, but also the challenges and recent failures in use of these tools. We set out to catalog important examples in healthcare AI, highlight best practices around AI development and implementation, and offer key points that need to be discussed for consensus to be achieved on how to address them as an AI community and society," said Matheny, an Associate Professor in the Department of Biomedical Informatics, and co-editor of AI in Healthcare: The Hope, The Hype, The Promise, The Peril.
For the immediate future, in health care, AI should be innovated and be thought of as a tool to support and complement the decision-making of highly trained medical professionals in delivering care in collaboration with patients and their goals, Matheny said. AI has an extremely wide potential and has so much more to offer in the medical industry once companies maximize its capabilities.
Recent advances in deep learning and related technologies have resulted in great success in interpreting medical images, such as radiology and retina exams. This has spurred a rush toward AI development that brought more opportunities for tech giants to innovate their works and receive profit. However, some of the tools have had problems with bias and prejudice from the populations they were developed from, or from the choice of an inappropriate target. Technological innovations and developments should also be centralized to the people marginalized sectors, since they are the most vulnerable to diseases and are in desperate need of assistance. Data analysts and developers need to work toward increased data access and standardization as well as thoughtful development, so algorithms aren't biased against already marginalized and weak patients.
"AI has the potential to revolutionize health care. However, as we move into a future supported by technology together, we must ensure high data quality standards, that equity and inclusivity are always prioritized, that transparency is use-case-specific, that new technologies are supported by appropriate and adequate education and training, and that all technologies are appropriately regulated and supported by specific and tailored legislation," the National Academy of Medicine wrote in a release.