Changing course: Healthcare AI systems are leaving clinicians behind
The adoption of AI in healthcare settings is accelerating rapidly, and a new paper published in Artificial Intelligence in Medicine argues that clinicians may not be able to keep pace with these changes. As AI technologies are deployed in everything from diagnostics to administrative support, the study's authors express concern that educational resources are inadequate for keeping clinicians up to speed.
According to the perspective titled Unprepared and Overwhelmed: A Case for Clinician-Focused AI Education, many of the available guides lack depth and are not aligned with the realities of medical settings, such as clinics and hospitals. As it stands, educational tools are loaded with technical concepts that clinicians may be unfamiliar with, as these programs are not written or designed with real healthcare staff in mind.
The researchers, led by Nadia Siddiqui, MD, from the University of Washington School of Medicine, were inspired to examine these educational programs following the 2020 launch of the Epic Sepsis Model. The model was rapidly deployed in hundreds of hospitals nationwide—after being internally validated in only three settings to improve sepsis detection.
The system was later found to perform poorly when externally validated by informatics physicians, highlighting the need for clinicians to be viewed as stakeholders in the AI adoption process—since they are ultimately the ones responsible for ensuring these systems function as intended.
Siddiqui, et al., encourage developers to take this lesson to heart in the development of any AI system, including by ensuring that instructional materials are tailored to the average doctor or nurse.
Here are three of their key pieces of advice:
1. More case-based learning catered to specialties.
The authors recommend that educational guides be tailored to specific specialties and backed by case study examples that make the uses of AI more relatable. They note that medical schools already operate this way—applying real-world grounding to concepts through hands-on education, as opposed to purely lecture-driven teaching.
“While case-based learning may have limitations in AI education, such as a need for variation in cases and a need for flexible design, guides should include real-world AI scenarios to increase comprehension and applicability. Currently, the vast majority of guides focus on explaining AI concepts to clinicians with brief examples, as opposed to clinical cases.”
2. Informatics physicians must lead AI education.
Informatics physicians are skilled at integrating technology into clinical workflows, bridging the gap between AI adoption and patient care. Any teaching initiative should ensure their direct involvement to maximize effectiveness.
“Leveraging the expertise of clinical informaticians secures the practicality and alignment of AI/ML training in medical education with clinical decision-making, ultimately improving patient outcomes, health system efficiency and fostering responsible implementation. Just as medical education relies on cardiologists to teach about the heart and pulmonologists for the lungs, we must rely on clinical informaticians to teach about AI in healthcare.”
3. Education needs to extend beyond clinicians.
Clinicians aren’t the only stakeholders in AI adoption. Hospital administrators, IT experts and regulators will also need to navigate these systems, and any educational effort must include them. The authors suggest that educational materials emphasize how AI will integrate into existing workflows and address concerns such as data governance and liability.
Aligning expectations for all stakeholders may begin by allowing students to familiarize themselves with these tools in the classroom.
“Moreover, it should be an initiative that ultimately finds its way into the classrooms of medical education and continuing medical education with specialty-specific AI education approaches. In recent decades, medical education has integrated clinically relevant innovations, such as advances in genetics and biostatistics, equipping students with essential skills and competencies for modern practice. Similarly, education on AI tools should be integrated into curricula, preparing future physicians for their growing role in patient care.”
The full paper is available here.
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Chad is an award-winning writer and editor with over 15 years of experience working in media. He has a decade-long professional background in healthcare, working as a writer and in public relations.