Recommended for M1-M4 students with an interest in artificial intelligence, data science, technology in medicine, or digital health.

Student Contact: Victoria Hodkiewicz, vhodkiewicz@luc.edu 

For AY2025-2026, Sessions will be held from 4:30-6:30 pm on the following dates: 

For AY2025-2026, Sessions will be held from 4:30-6:30 pm on the following dates: 

September 3rd
•    Sept 24th
•    November 5th
•    Dec 3rd
•    January 14th
•    Feb 11th

Each month, students will attend a live in-person lecture followed by a small group discussion session, where they will explore topics such as biomedical informatics fundamentals, clinical decision support, and ethical considerations in AI. These discussions encourage active participation, allowing students to share perspectives and delve into how AI can impact clinical practice. Students will also be assigned relevant readings and materials on the month’s topics to provide a foundation for the lectures and discussions. To reinforce learning, students will complete a quiz each month covering key concepts and applications, helping them track their understanding. Students will also attend the Center for Health Outcomes and Informatics Research (CHOIR) seminar series, where they will engage with experts in AI and health informatics, broadening their knowledge of cutting-edge research and applications. The course culminates in a team project, where groups will analyze an AI application in a specific healthcare domain—such as radiology, public health, or clinical decision support—examining its benefits, limitations, and ethical concerns. Teams will present their findings and recommendations in a final presentation to the class and a panel of evaluators, fostering collaborative learning and real-world application of AI in healthcare.

I. Introduction
This elective provides a comprehensive introduction to artificial intelligence (AI) and its applications in healthcare, focusing on foundational knowledge, practical skills, and critical thinking about AI-driven tools. Students will explore AI’s transformative role in medical fields like radiology, predictive analytics, and public health while addressing ethical implications, data privacy concerns, and transparency in AI-based decisions. The course prepares future physicians to integrate AI responsibly and effectively into clinical practice and healthcare systems.
II. Course Objectives
1.    Medical Knowledge
a.    Define core concepts in biomedical informatics and AI, exploring key applications in clinical fields such as radiology, dermatology, and predictive analytics.
b.    Understand the principles of machine learning and AI, including their capabilities and limitations in healthcare.
c.    Analyze ethical considerations in AI, including data privacy, security, and bias.
2.    Patient Care
a.    Apply AI principles to clinical scenarios, understanding its potential to enhance diagnostic and therapeutic outcomes.
b.    Explore the role of AI in improving patient safety, quality of care, and healthcare efficiency.
3.    Interpersonal and Communication Skills
a.    Develop communication strategies for discussing AI’s applications and limitations with interdisciplinary teams, patients, and families.
b.    Enhance trust and transparency when explaining AI-driven healthcare decisions.
4.    Professionalism
a.    Demonstrate respect for ethical standards in using AI, ensuring patient autonomy, privacy, and informed consent.
b.    Embrace inclusivity and cultural sensitivity in technology-driven healthcare applications.
5.    Practice-Based Learning and Improvement
a.    Reflect on personal learning needs in AI and data science to adapt to evolving technologies.
b.    Critically evaluate AI applications to responsibly integrate data-driven approaches in clinical care.
6.    Systems-Based Practice
a.    Examine the role of health information systems and standards (e.g., ICD, SNOMED, FHIR) in integrating AI into clinical practice.
b.    Assess resource allocation and logistical challenges of AI in various healthcare settings.
7.    Interprofessional Collaboration
a.    Collaborate with professionals from fields such as data science and engineering, recognizing their roles in AI integration.
b.    Embrace a team-based approach to implementing AI solutions in healthcare.
8.    Personal and Professional Development
a.    Commit to lifelong learning in AI and informatics, acknowledging their impact on the future of medicine.
b.    Reflect on the role of AI in personal and professional growth, preparing to responsibly lead its integration into clinical practice.

III. Prerequisites
None; recommended for M1-M4 students with an interest in AI, data science, or digital health.

IV. Course Logistics
Duration: Asynchronous elective; equivalent to 1 week of credit.

Periods Offered: PT-M1, PT-M2, PT-M3, PT-M4:  Six sessions will be held between August and February each year. . Students are expected to join all sessions (If unable to attend 1-2 lectures, alternative assignments will be provided on a case by case basis)

Schedule: Monthly live in-person sessions followed by discussions.

Students will be evaluated on the following:
•    Attendance and participation in required lectures and discussions (35%).
•    Attendance at CHOIR seminar series (5%).
•    Reflection Paper (20%).
•    Mid-rotation feedback (5%).
•    Monthly Quizzes (15%).
•    Final Project and Presentation (20%).

Grading will be on a Pass/Fail basis. The passing score for quizzes is 70%.