Artificial Intelligence Applications in Healthcare

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Department	Interdisciplinary Medicine
Course Number	IDIS 377
Course Title	Artificial Intelligence Applications in Healthcare
Course Director	Ioannis Koutroulis, MD, PhD, MBA; Kirsten M. Brown, PhD, MA
Length (Weeks)	2
When Offered	TBD
Prerequisites	Completion of preclinical curriculum
Availability Notes	Upcoming elective (as of January 2025); availability TBD
Contact Name	Dr. Koutroulis
Contact Phone	202-994-3190
Contact Fax
Contact Email	ikoutroulis@gwu.edu
Other Contacts	Dr. Brown (kmbrown@gwu.edu; 202-994-6705)
Location
Limit	20
Report
Evaluation	Grading: Pass/Fail, based on participation and assignments. In order to pass the elective, students will need to pass each of the following components: Participation in Discussions Written Reflection Prompts Weekly Case Assignments (graded for completion) Final Project: Clinical Case Additional assignment details and grading rubrics are available in the elective syllabus.
Description	Purpose and Rationale for the Course: This course aims to equip medical students with a foundational understanding of the potential applications of artificial intelligence (AI) in healthcare, including its limitations, and ethical considerations regarding its use through an equity lens. As AI becomes increasingly integrated into various aspects of clinical care, research and medical education, future physicians must be prepared to interpret AI-driven tools, understand their role and accuracy as well as ethical implications. This course will enhance students’ ability to critically evaluate sample products of AI applications and contribute to healthcare innovation as future providers and researchers. Target Students: 3rd- and 4th-year MD students Pre-requisites: Completion of preclinical curriculum to allow students to better contextualize AI’s applications in real-world clinical, educational, and professional settings. Course Description: This course introduces medical students to artificial intelligence’s evolving role in healthcare and medical education. Through lectures, case studies and discussions, students learn to assess AI’s benefits and limitations in various contexts, including clinical research, patient care and education. Ethical discussions and critical evaluation of AI tools are key components, ensuring that students emerge as informed users and advocates for responsible AI in medicine. Course Learning Objectives: By the end of this course, the student should be able to: Explain fundamental AI concepts, including machine learning (ML), natural language processing (NLP), and predictive modeling, and their roles in different AI applications used in medical education, clinical operations, and research. Analyze case studies of AI applications and examine for accuracy and usefulness. Identify ethical considerations, including data privacy, bias, and accountability, in the use of AI in medicine. Evaluate the limitations and potential errors of AI-driven tools and their impact. Practice using AI tools for research, clinical, and educational endeavors.
Additional Notes