AMA: Take extra care when applying AI in medical education

Brendan Murphy , Senior News Writer

Citing the potential to improve both the quantity and quality of patient care, the AMA House of Delegates (HOD) has adopted new policy that further examines augmented intelligence (AI) and its potential to benefit for doctors and physicians in training.

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“To realize the benefits for patient care, physicians must have the skills to work comfortably with augmented intelligence in health care. Just as working effectively with electronic health records is now part of training for medical students and residents, educating physicians to work effectively with AI systems, or more narrowly, the AI algorithms that can inform clinical care decisions, will be critical to the future of AI in health care,” said AMA Board Member S. Bobby Mukkamala, MD.

The new policy comes a year after the Association adopted its first policies related to AI during the 2018 AMA Annual Meeting.

The AMA “has long demonstrated a commitment to developing and supporting disruptive advancements in medical education, both autonomously and in partnership with others,” notes the AMA Council on Medical Education report whose recommendations the HOD adopted.

“This commitment can be seen in the Council on Medical Education’s contributions to the 1910 Flexner Report, the establishment of many of the leading U.S. medical education organizations that exist today, the groundbreaking Accelerating Change in Medical Education Consortium, the newly launched Reimagining Residency initiative, and enhanced e-learning content design and delivery,” the report said.

As further evidence of that commitment, the HOD directed the AMA to encourage:

  • Accrediting and licensing bodies to study how AI should be most appropriately addressed in accrediting and licensing standards.
  • Medical specialty societies and boards to consider production of specialty-specific educational modules related to AI.
  • Research regarding the effectiveness of AI instruction in medical education on learning and clinical outcomes.
  • Institutions and programs to be deliberative in the determination of when AI-assisted technologies should be taught, including consideration of established evidence-based treatments, and including consideration regarding what other curricula may need to be eliminated in order to accommodate new training modules.
  • Stakeholders to provide educational materials to help learners guard against inadvertent dissemination of bias that may be inherent in AI systems.
  • The study of how differences in institutional access to AI may impact disparities in education for students at schools with fewer resources and less access to AI technologies.
  • Enhanced training across the continuum of medical education regarding assessment, understanding, and application of data in the care of patients.
  • The study of how disparities in AI educational resources may impact health care disparities for patients in communities with fewer resources and less access to AI technologies.
  • Institutional leaders and academic deans to proactively accelerate the inclusion of nonclinicians, such as data scientists and engineers, onto their faculty rosters in order to assist learners in their understanding and use of AI.
  • Close collaboration with and oversight by practicing physicians in the development of AI applications.