Artificial intelligence is transforming how physicians learn, conduct research, make clinical decisions and deliver patient care. At Indiana University School of Medicine, the Artificial Intelligence in Medicine scholarly concentration gives medical students an opportunity to explore this rapidly evolving field while considering not only what AI can do, but how it can be used responsibly and effectively in medicine.
Students examine AI from multiple perspectives, including its clinical applications, research potential, educational uses, ethical considerations and role within health systems. No previous experience with AI or machine learning is required — just curiosity and an interest in understanding how emerging technology can shape the future of medicine.
Fast facts about the concentration
Location: Statewide
Co-directors: Anthony Shanks, MD, MS, MEd, and Andrew Gonzalez, MD, JD, MPH
Prior AI experience required: None
Meet the co-directors
The Artificial Intelligence in Medicine scholarly concentration is co-directed by Anthony Shanks, MD, MS, MEd, and Andrew Gonzalez, MD, JD, MPH, who bring complementary expertise in medical education, clinical medicine, research and technology.
Shanks is senior associate dean for Medical Student Education at IU School of Medicine. His academic career has focused on innovation in medical education and bringing together pedagogy and technology. He has led faculty development on AI in medical education, co-created AI tools and conducted scholarship examining their use. What excites Shanks most is the concentration's broad approach to the technology. Students don't simply learn about AI tools; they critically examine their implications.
Gonzalez is a vascular surgeon-scientist-developer at IU School of Medicine and associate director for data science at the Regenstrief Institute's Center for Health Services Research. His research focuses on developing and validating AI-enhanced clinical decision support tools. His ALCHEMI AI Lab focuses on multimodal, multitask deep learning using real-world clinical data.
Gonzalez is particularly excited to work with students interested in using artificial intelligence and machine learning to improve patient care. Through this work, he hopes to help pioneer a new kind of physician: the clinician-scientist-developer, who can bridge the worlds of clinical medicine, research and technology.
What students can expect
Students explore how AI and machine learning are being applied across medical education, research, clinical care and health systems. Topics and potential areas of exploration include machine learning in clinical medicine, clinical decision-making tools, AI-assisted assessment, educational applications and research tools.
Just as importantly, students learn to critically evaluate these technologies. The concentration examines AI from multiple angles, giving students opportunities to consider its capabilities, limitations and ethical implications as they investigate how it can be safely and effectively applied in medicine. Students also get to work with faculty to complete scholarly work that allows them to explore AI in an area that aligns with their interests and professional goals.
Is this the right fit?
Students do not need any AI experience to participate in the concentration; only curiosity that comes with wanting to learn more about this technology and to apply it to the field of medicine.
The goal is to help students become clinico-technical “bilinguals” — physicians who understand both the clinical and technological dimensions of AI. That foundation can prepare students for a range of future opportunities, from becoming clinical AI leaders within health systems and contributing clinical expertise to technology companies to developing health care startups or helping shape AI policy.
Examples of student research
Students can pursue scholarly projects exploring the use and evaluation of artificial intelligence across medicine. Examples include:
- Integration of a retrieval-augmented generation (RAG) chatbot with a commercial educational tool
- Assessment of frontier AI models' understanding of the clinical management of carotid artery disease
- Machine learning applications in clinical medicine
- AI-supported clinical decision-making tools
- AI in objective structured clinical examination (OSCE) assessment
- AI applications in anatomy education
- Cognitive psychology and AI-supported educational tools
- Machine learning applications in obstetric and gynecologic ultrasound
For students who want to understand — and help shape — how artificial intelligence will influence the future of health care, the Artificial Intelligence in Medicine scholarly concentration offers an opportunity to begin building that expertise during medical school.
