Medical educators encouraged to equip physicians-to-be with 4 AI competencies

Researchers at the University of Illinois Chicago have developed an elective course that can quickly transform fourth-year medical students from functional AI novices to budding AI experts.

Trying the four-week curriculum on two separate cohorts, the team found participants boosted average AI knowledge scores from 61% pre-course to 97% post.

Describing the project in the July edition of the Journal of the American Medical Informatics Association, corresponding author Jacob Krive, PhD, and colleagues suggest four competencies that medical-school faculty could instill in their students with similar success:

Competency 1. Evaluator. Key skill: Ability to discern when a technology is appropriate for a given clinical context and what inputs are required for meaningful results. Learning objectives:

• Explain the foundational concepts of medical analytics AI and explainable AI.
• Describe a clinical scenario in which an AI solution can augment and improve clinical processes.

Competency 2. Critical AI systems appraiser. Key skill: Ability to explain how various forms of artificial intelligence are applied today in healthcare to augment and improve health outcomes. Learning objectives:

• Build and appraise use cases of applied AI in value-based care being used by health systems today.

Competency 3. Interpreter. Key skill: Ability to assess AI inputs and outputs with a reasonable degree of accuracy, including knowing potential sources of error, bias or clinical inappropriateness. Learning objectives:

• Interpret common AI terms and components (algorithms, machine learning vs. deep learning, etc.)
• Define and describe linkages between evidence-based medicine, real-world evidence, medication safety, predictive analytics, mobile computing, artificial intelligence.

Competency 4. Communicator. Key skill: Ability to convey results and underlying process in a way that patients as well as other health professionals can understand. Learning objectives:

• Discuss leading practices in data integration needed to deploy analytic solutions in healthcare.
• Identify the drivers, decision factors and collaboration required across clinical, operational and technology teams to realize return on investment in AI.

Krive and co-authors note that, post-course, participating students demonstrated a good grasp of ways their fast-gained understanding might apply during upcoming residencies and, by extension, over the course of their respective careers in medicine.

The authors’ concluding comment:

“Education regarding the incorporation of AI technologies in routine clinical care, if well done, will give the gift of time to patients and physicians. This time will enable physicians to foster meaningful and therapeutic relationships while utilizing AI technologies to ensure the provision of optimal care and completion of more mundane tasks.”

JAMIA has posted the paper in full for free.