Institutions should develop and implement AI curricula through interdisciplinary collaboration, bringing together experts from medical education, computer science, ethics, sociology, and other relevant fields. These curricula should address both specialty-specific AI applications and role-specific competencies, preparing health care professionals to utilize AI within health care teams effectively. This integrated approach ensures learners develop practical AI skills relevant to their specialties while understanding their role in team-based health care delivery.
From Principle to Practice
Apply this principle to your practice using the following strategies:
- Include foundational AI literacy in medical education curricula. Institutions should consider designing comprehensive curricula that prepare health care professionals to leverage AI effectively within their specialties and in team-based care. A core element of the curricula should be foundational education and training in AI literacy (e.g., foundations of machine learning, natural language processing, and predictive analytics), the ability to interpret AI-driven data outputs, and critical assessment of accuracy, reliability, and trustworthiness. In addition to teaching about opportunities for AI’s use, it is equally important to teach the limitations and biases inherent in AI systems and their implications for specific use cases in medicine.
- Develop advanced AI curricula tailored to specialties. To maximize the impact of AI in health care, advanced curricula should be tailored to address the specific needs of various medical specialties. For instance, radiology curricula can focus on AI-powered image interpretation, triage systems, and diagnostic support, equipping radiologists to leverage these tools for faster and more accurate assessments. Surgical education and training can incorporate AI applications for preoperative planning, such as 3D modeling and risk assessment, as well as intraoperative guidance through robotics and augmented reality. In public health, learners can explore how AI enables population-level data analysis, predictive modeling for disease outbreaks, and the optimization of resource allocation. All physicians should be educated in the basics of using AI to document patient interactions to increase direct physician-patient contact time. By integrating such specialized content, medical education can prepare professionals not only to adopt AI but also to collaborate with developers to refine these tools, ensuring they align with clinical workflows and enhance patient care across specialties.
- Cultivate interdisciplinary collaboration in curriculum design. Development of AI curricula in medical education demands a collaborative effort by experts from diverse fields including, but not limited to, AI, data science, ethics, medical education, and clinical practice. Such interdisciplinary collaboration ensures that a curriculum is technically robust, current, and aligned with the practical needs of health care professionals. This collaboration is also critical for addressing the multifaceted nature of AI in health care, aligning curricula not only with the practical needs of health care professionals but also with the technological, ethical, and sociocultural dimensions of AI applications.
Medical institutions also should consider establishing partnerships with leading AI organizations, academic programs, and industry stakeholders to ensure access to cutting-edge content and practical tools. Providing funding and infrastructure to support collaboration between medical and AI disciplines — such as simulation labs, joint research, and training programs — enables the development of innovative, practical AI curricula. These efforts ensure that learners are prepared to excel in AI-augmented health care environments. - Identify AI learning needs for team-based care. Identifying AI learning needs for health care professionals across clinical care teams is essential to ensure coordinated patient care. Moreover, AI-enabled health care often requires collaboration between professionals with varying expertise, such as data scientists and clinicians. Curricula should foster team-based learning environments to simulate real-world scenarios where interprofessional collaboration is essential. Learners should be prepared to work on health care teams that might include physicians, nurses, pharmacists, care managers, and other administrative personnel. Role-playing exercises, group projects, and simulated AI-augmented workflows can enhance learners’ ability to work in diverse teams effectively.
- Develop outcome metrics. Developing appropriate outcome metrics that encompass both individual learner assessments and program evaluations is essential for measuring the effectiveness of AI integration in medical education and practice. This requires collaboration among educators, measurement scientists, clinicians, data scientists, and policymakers to create a framework that reflects both educational objectives and real-world impacts. Metrics should be designed to assess not only technical proficiency but also the ethical, collaborative, and clinical competencies necessary for effective AI use.
Develop standardized benchmarking tools and assessment rubrics to evaluate learners’ competency in AI utilization and critical appraisal. Create practical assessment scenarios that test learners’ ability to appropriately integrate AI tools into clinical decision-making while recognizing the tools’ limitations. Design structured exercises that require learners to demonstrate understanding of AI capabilities, potential biases, and safe implementation strategies. Include both knowledge-based assessments and performance-based evaluations that measure learners’ ability to effectively navigate AI tools in realistic clinical scenarios.