Description and List of modules
The Artificial Intelligence in Preventive Medicine & Public Health Certification Course is a 10-module, self-paced program designed to equip clinicians with the foundational knowledge and practical skills needed to apply AI responsibly in healthcare and public health settings. Participants will explore key topics such as AI and data literacy, machine learning fundamentals, large language models, ethical considerations, health equity, regulatory and legal frameworks, and interdisciplinary collaboration. The course emphasizes the selection, implementation, and management of AI tools that align with clinical goals while ensuring patient privacy, transparency, and equity. Through real-world case studies and structured learning, physicians will learn how to interpret AI-generated data, integrate tools into clinical workflows, assess potential bias, and navigate evolving legal and compliance standards. The program provides 11.5 Continuing Medical Education (CME) credits and a Focused Practice Designation upon completion. A comprehensive final exam follows the 10 modules and must be completed to receive the certification.
Course Modules Include:
- Introduction to AI in Preventive Medicine & Public Health Certification
- Fundamentals of AI and Data Literacy at the PM Level
- Fundamentals of AI & Machine Learning
- AI Ethical Considerations and Bias
- The Impact of AI (5A, B, and C)
- AI Regulatory, Legal, Privacy, and Copyright Considerations (6A & 6B)
- Selection of and Management of AI
- Interdisciplinary Teams for AI
- Applications of AI
- Summary of AI in Preventive Medicine & Public Health Certification
Module 1: Introduction to Artificial Intelligence in Preventive Medicine & Public Health
This module provides an overview of the course, its structure, and learning objectives. It introduces the role of AI in healthcare and public health and outlines how the certification program will build foundational knowledge and practical skills to support responsible AI adoption across clinical and public health environments. Learners will gain an understanding of the potential impact of AI on healthcare delivery, population health, and the healthcare workforce.
Module Lead: Helga Rippen
Module 2: Fundamentals of AI and Data Literacy at the PM Level
Participants will develop skills in interpreting and evaluating data used in AI systems. Topics include data sources, data quality, limitations, and how data biases can influence AI outputs. A solid grasp of data literacy is critical to understanding the strengths and weaknesses of AI-generated insights and to identifying when data may misrepresent patient populations or clinical needs.
Module Leads: Linette Scott and Sristi Sharma
Module 3: Fundamentals of AI & Machine Learning
This module introduces the foundational concepts of AI and machine learning, including key terminology such as algorithms, neural networks, training data, and supervised versus unsupervised learning. Understanding how these technologies work is essential for evaluating how AI systems analyze data, identify patterns, and generate predictions or recommendations in clinical and public health contexts.
Module Leads:
3A: Shashank Nayak, Jacob Vanhouten, and Jonas Schoettler
3B: Yahya Shaikh
Module 4: Ethical Considerations and Bias
This module explores the ethical challenges in AI, such as fairness, transparency, accountability, and the risk of bias. Physicians will learn how to evaluate the ethical use of AI tools and apply ethical frameworks when considering AI’s impact on care delivery, particularly for vulnerable and underserved populations.
Module Leads: Cole Zanetti and Brad Thornock
Module 5: Impact of AI
This module focuses on how AI can both alleviate and exacerbate health disparities. Learners will explore strategies to ensure equitable AI implementation and the importance of inclusive data sets. Submodules address equity-focused design, workforce implications, and patient, clinician, and public engagement in shaping AI applications.
Module Leads:
5A (Health Equity): Christine Kang and Fonthip Watcharaporn
5B (Workforce): Cole Zanetti
5C (Public Health, Clinician & Patient/Citizen): Prasad Acharya
Module 6: Regulatory, Legal, Privacy and Copyright Considerations
This module examines the regulatory and legal environment for AI in healthcare. Topics include HIPAA, FDA regulations for AI-enabled tools, liability, intellectual property, and data ownership. Learners will gain an understanding of compliance obligations and how legal frameworks shape the safe and responsible use of AI.
Module Leads:
6A (Regulatory and Legal): Jeremy Mandia and Case Keltner
6B (Privacy and Copyright): Richard Bruno
Module 7: Management of AI
Learners will explore how AI tools can be integrated into existing clinical workflows and operations. This includes identifying clinical tasks suitable for AI support, interpreting AI recommendations, applying change management techniques, and ensuring that AI enhances rather than disrupts care delivery. Practical strategies for implementation and performance monitoring are also discussed.
Module Leads: Jonas Schoettler, Fonthip Watcharaporn, Sumedh Mankar
Module 8: Interdisciplinary Collaboration
This module emphasizes the importance of cross-disciplinary collaboration in AI implementation. Physicians will learn how to effectively communicate and collaborate with data scientists, software developers, and healthcare administrators to ensure the development and deployment of AI tools are aligned with clinical needs and operational goals.
Module Lead: Tauhid Mahmud
Module 9: Applications of AI Tools
Focusing on real-world applications, this module reviews how AI is currently being used in preventive medicine and public health, including diagnostics, surveillance, risk prediction, and population health management. Learners will also explore the importance of staying current with technological advancements through ongoing education and adaptation.
Module Leads: Jonas Schoettler, Fonthip Watcharaporn,Tauhid Mahmud
Module 10: Course Summary and Final Review
The final module provides a synthesis of key takeaways from the course and offers a structured review of content. Learners will revisit core principles and prepare for the final comprehensive exam. The module reinforces the importance of ethical, equitable, and effective AI use in healthcare practice and policy.
Module Lead: Helga Rippen