Courses

This course introduces medical students to the foundational concepts of artificial intelligence and the ethical and response use of AI. During the course, explore key principles of machine learning, predictive analytics, generative AI and large language models, as well as gain insight into how these tools are used and how they can be leveraged in Phase 1 of medical school. Through interactive workshops and hands-on activities, students will have the opportunity to apply AI technologies to their own learning and use these tools to develop a research question.

Course Director: Anthony Shanks, MD, MS, MEd
Email: ashanks@iu.edu
Phone: 314-603-0262
Home campus: Indianapolis/Statewide
Type of course: Hybrid

Learning objectives

By the end of this course, a student will be able to:

1. Understand and apply core learning theories (retrieval practice, spaced repetition, interleaving). (MK, PBLI)
2. Explore ethical considerations of AI in education. (P, HEAL, SBP) 
3. Gain practical skills using AI tools to enhance learning and assessment. (PBLI, MK, SBP, ICS)

Course activities: Pre-reading assignments, hands-on activities, prompt design, learning science applications, analysis of ethics and governance, educational assessment design and research productivity workflows

Estimated time distribution: Online (25%), Lecture/Seminar (50%), and Laboratory or Scholarly Research (25%)

Assessments: Attendance at teaching sessions (50%), homework assignments from each session — five total (40%), and identification of research question reviewed by course directors (10%)

Prerequisites: Scholarly Concentration enrollment

Interprofessional collaboration: None

This course focuses on the principles and practice of using AI in clinical environments, allowing students to examine how decision support systems can aid diagnostic and therapeutic reasoning, recognize their limitations and apply appropriate levels of human oversight within everyday workflows.

Ethical and regulatory responsibilities, including HIPAA‑compliant data handling, de‑identification, secure tool selection and FERPA considerations, are emphasized. Students will evaluate tools for accuracy, safety, bias and clinical appropriateness. This course will help students develop a fully formed AI-related research question and be paired with a faculty mentor to begin their scholarly concentration project.

Course Director: Anthony Shanks, MD, MS, MEd
Email: ashanks@iu.edu
Phone: 314-603-0262
Home campus: Indianapolis/Statewide
Type of course: Hybrid

Learning objectives

By the end of this course, a student will be able to:

1. Understand AI’s role in diagnostic and therapeutic decision-making. (MK, PCSBP, PBLI) 
2. Understand tradeoffs between human in the loop, human on the loop and human at the end clinical decision-making. (SBP, PC, HEAL) 
3. Learn principles of machine learning relevant to clinical practice. (MK, PBLI, SBP) 
4. Explore integration of AI into Electronic Medical Records. (SBP, PC, ICS, P) 
5. Critically evaluate AI tools for accuracy, safety, bias and effectiveness. (PBLI, HEAL, P, SBP)

Course activities: Pre-reading assignments.

Students will apply artificial intelligence concepts through hands-on, case-based activities that connect AI to clinical practice and research. Activities include identifying clinical workflow challenges and proposing AI-enabled solutions, evaluating specialty-specific AI applications and using AI decision-support tools in simulated patient cases. Students will examine ethical, regulatory and implementation considerations, including bias mitigation, HIPAA compliance and FDA oversight. They will also compare AI approaches, including inference-only models and retrieval-augmented generation (RAG) workflows, using clinical guidelines. Learn from researchers at IU School of Medicine how AI transforms their work.

Estimated time distribution: Online (25%), Lecture/Seminar (50%) and Laboratory or Scholarly Research (25%)

Assessments: Attendance at teaching sessions (50%), homework assignments from each session — 5 total (40%), and identification of research question reviewed by course directors (10%) 

Prerequisites: Scholarly Concentration enrollment

Interprofessional collaboration: None