The role of artificial intelligence, or machine learning, will be pivotal as the healthcare industry wrestles with a gargantuan amount of data that could improve - or muddle - health and cost priorities, according to a National Academy of Medicine Special Publication on the use of AI in healthcare.
Yet, the current explosion of investment is happening without an underpinning of consensus of responsible, transparent deployment, which potentially constrains its potential.
The new report is designed to be a comprehensive reference for organizational leaders, healthcare professionals, data analysts, model developers and those who are working to integrate machine learning into healthcare, said Michael Matheny, MD, MS, MPH, associate professor in the Department of Biomedical Informatics for Vanderbilt University Medical Center.
"It's critical for the healthcare community to learn from both the successes, but also the recent failures, in use of these tools," Matheny said. "We set out to highlight best practices around AI development and implementation."
Matheny underscores the applications in healthcare look nothing like the mass market imagery of self-driving cars that is often synonymous with machine learning or tech-driven systems.
"For the immediate future in healthcare, AI should be thought of as a tool to complement the decision-making of trained professionals in delivering care in collaboration with patients," Matheny said.
Recent advances in deep learning have met with great success in imaging interpretations, such as radiology and retina exams, which have spurred a rush toward AI development that brought first, venture capital funding, and then industry giants. However, some of the tools have had problems with bias from the populations they were developed from or from the choice of an inappropriate target. 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 patients.
"The editors hope this report can contribute to the dialog of patient inclusivity and fairness in the use of AI tools, and highlight the need for careful development and implementation to optimize their chance of success," Matheny said.
Matheny along with Stanford University School of Medicine's Sonoo Thadaney Israni, MBA, and Mathematica Policy Research's Danielle Whicher, PhD, MS, penned an accompanying piece for JAMA Network about the watershed moment in which the industry finds itself (link online at BirminghamMedicalNews.com).
"AI has the potential to revolutionize healthcare. 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.
"I want people to use this report as a foil to hone the national discourse on a few key areas including education, equity in AI, uses that support human cognition rather than replacing it, and separating out AI transparency into data, algorithmic, and performance transparency," Matheny said.
