U.S. medical schools are revamping their curricula to prepare students and clinicians for health system AI and machine learning.
State medical licensing boards do not currently require formal AI proficiency. However, the medical education community is moving ahead with changing times. Last year, the American Medical Association adopted policy directing the adoption of AI training in medical education and continuing education for physicians.
While the AMA has taken its stance, the body accrediting medical schools, Liaison Committee on Medical Education, has not established AI literacy as a formal graduation requirement. Due to the lack of standardized national mandate, AI training has been fragmented across institutions.
Implementing these programs often requires major investments in software licenses, computing infrastructure, faculty training and curriculum development. As a result, institutions are adopting different strategies based on the resources available to them.
This Fragmented Access Could Negatively Impact Specific Communities
Uneven access to AI training could widen existing disparities in medical education and patient care.
Well-resourced medical schools are better positioned to expand AI instruction and clinical tools. Schools with fewer resources may struggle to keep pace.
Long-term outcomes have not yet been reported in research. However, experts caution that this gap could lead to uneven AI literacy among physicians, potentially affecting how care is delivered across different communities.
The Top-Tier Medical Schools Leading the Charge in AI Clinician Training
Established research in cognitive science and clinical decision-making shows that medical trainees are vulnerable to automation bias. Without training, trainees have a documented tendency to over-rely on algorithmic recommendations even when they conflict with clinical judgment and guidelines.
Some of the nation’s top medical schools have already launched dedicated AI initiatives, while others have created standalone dual-degree programs. Other institutions are adding coursework on machine learning for clinical decision support, algorithmic bias and data privacy to their courses.
Stanford University School Of Medicine
Stanford University has adopted one of the nation’s most comprehensive approaches to AI in medical education. Last year, the university revised its curriculum to incorporate AI into classroom teaching, clinical education and physician training. A steering committee and working group of AI researchers, programmers, clinical faculty and students developed the curriculum.
The program focuses on foundational AI literacy, including the different types of AI systems and how clinicians can use AI tools in practice. Students also learn about the ethical and legal implications of AI in medicine and how to critically evaluate AI-generated information.
The Bay-area university developed several AI platforms to improve students’ strengthen clinical reasoning and patient communication skills. The university also developed Clinical Minds AI, a chatbot platform that allows students to practice interviewing simulated patients across a range of clinical scenarios.
In addition, Stanford University also developed AI Clinical Coach, which listens to simulated patient encounters and provides feedback to trainees. Students present patient cases to faculty instructors, while the tool analyzes the interaction and generates feedback to help guide coaching and personalized learning.
Morehouse School Of Medicine
Morehouse School of Medicine has taken a faculty-first approach to AI integration. The college has adopted an equity-centered focus, training educators on AI before scaling student-facing AI instruction. A core component of this faculty training is identifying and mitigating data, selection and interpretation biases within AI.
The college launched professional development tracks for its medical faculty. The Teaching and Learning through Artificial Intelligence course trains medical educators to use generative AI to create personalized patient simulation scenarios and design AI-integrated curricula.
Morehouse School of Medicine also established the Center of Excellence for Digital Health and the Center of Excellence for the Validation of Digital Health Technologies and Clinical Algorithms to advance equitable innovation. The centers focus on evaluating clinical algorithms, AI/ML models and digital health technologies for bias, particularly as they affect underserved and underrepresented populations.
Supported through Novartis’ Beacon of Hope Initiative, the centers engage students and researchers in validating clinical algorithms and AI-powered diagnostic tools for racial and systemic bias. Morehouse has also partnered with the National Institutes of Health’s AIM-AHEAD initiative to advance inclusive AI research and support the development of AI/ML models that better serve underserved communities.
Morehouse School of Medicine also regularly hosts demonstrations, showcases and interdisciplinary events that expose medical students to real-world AI applications.
Columbia University Vagelos College Of Physicians And Surgeons
Columbia University has focused on aggressive integration of AI into curriculum. The institution launched an AI at VP&S Initiative to lead the adoption.
A central component of the initiative is the integration of AI education into the core medical curriculum. The medical school curriculum features foundational knowledge on AI data science, large language models and predictive analytics for clinical decision-making. The lessons will also include training on AI tools, model limitation and capabilities, including ethical implications of AI-enabled technologies.
The institution has also expanded professional development opportunities for practicing clinicians and faculty through structured training modules on AI in clinical care. These programs cover topics, such as evaluating AI-generated outputs, understanding model limitations and bias, safeguarding patient privacy, and assessing the appropriate use of AI tools in healthcare settings.
Howard University College Of Medicine
Howard University does not currently offer a standalone AI course for medical students. Instead, the school has emphasized research opportunities and exposure to emerging AI technologies through interdisciplinary programs and faculty-led initiatives.
One of the university’s most prominent efforts is the AI-Driven Computational Medicine and Smart Health Lab, where medical students can apply for research opportunities focused on the use of artificial intelligence in healthcare. Students who participate gain experience exploring AI applications in disease prediction, precision medicine, addiction research, personalized care and population health.
The lab uses clinical data, machine learning and computational modeling to address a range of health challenges. Current projects include decision-support tools for sickle cell disease, colorectal cancer detection, Alzheimer’s disease interventions, and research on data privacy and security.
Harvard Medical School
Harvard Medical School integrated AI into its MD curriculum, with a focus on AI literacy, clinical reasoning and critical evaluation of AI-generated information. However, the university has primarily focused on integrating AI within its teaching hospitals.
The medical school updated its MD curriculum to teach students how to use AI tools in clinical settings and understand the fundamentals of how AI works. Students seeking more advanced training can pursue the Harvard-MIT Health Sciences and Technology pathway. All MD students also complete a monthlong capstone course focused on AI in health care.
For practicing clinicians, physicians and health care executives, the university offers virtual and hybrid AI courses. Harvard Medical School also provides self-paced continuing education courses on AI in medicine through its HMX online learning platform.
UC Davis School of Medicine
UC Davis School of Medicine has emphasized balancing AI integration with core clinical and academic training.
The school incorporates AI and clinical informatics into its MD curriculum through the I-EXPLORE program. Fourth-year students can also collaborate with faculty on AI-related research projects.
Students are trained to understand how AI systems generate clinical outputs so they can critically evaluate algorithmic recommendations. They are also introduced to digital tools that support diagnostic reasoning, patient communication and education on social determinants of health.
At the same time, UC Davis School of Medicine maintains limits on AI use in medical education. Students may only use approved tools in designated assessments. In general, AI use is permitted for limited tasks, such as brainstorming, outlining and minor editing of written work.
Icahn School of Medicine at Mount Sinai
Icahn School of Medicine at Mount Sinai was the first medical school to implement enterprise access to AI tools.
In 2025, the medical school partnered with OpenAI to deploy ChatGPT Edu for all medical school students and graduate programs. The initiative includes training on the responsible use of generative AI. Students can use the platform as a learning aid to explore clinical cases, diagnostic reasoning and research-related activities.
Rather than creating a standalone AI course, Icahn School of Medicine at Mount Sinai focused on integrating AI literacy and practical applications into existing courses. The medical school also expanded AI education to faculty, clinicians and educators.
Every month, the school also hosts workshops and training sessions focused on generative AI, AI-assisted teaching strategies and responsible AI use in healthcare and academic settings.
University of Texas Health Science Center at San Antonio
The University of Texas has specialized AI education through standalone courses, electives and graduate degree programs.
The university was the first in the United States to launch a five-year dual-degree program that combines a Doctor of Medicine degree with a Master of Science in Artificial Intelligence. Students admitted to the program complete the standard medical curriculum during their first three years of training. In their fourth year, they complete the in-person master’s program in AI. After that, they return to finish their final year of clinical training.
Medical students who do not pursue the dual degree can still take AI-focused enrichment electives during the preclinical years. These courses introduce core concepts in artificial intelligence and their applications in medicine.
Students in later stages of the MD program can enroll in elective rotations focused on generative AI, clinical decision support systems and health data interoperability. The courses emphasize practical applications of AI in patient care, research and health system operations.
Emory Medical School
Emory Medical School’s AI efforts are concentrated in clinical research, health system implementation and its hospitals.
The university has not announced an updated core curriculum for all standard medical students. However, Emory Medical School offers a Medical Informatics Track for residents and fellows. The The track covers health information systems, clinical informatics, AI in clinical workflows, clinical decision support and implementation of informatics solutions.
In 2023, the medical school launched the Emory Empathetic AI for Health Institute to advance the development and integration of AI and machine learning tools into clinical care. The institute develops and evaluates AI technologies designed to improve patient care, health system operations and access to care.
