Artificial Intelligence and Machine Learning for Primary Care Curriculum (AiM-PC)

Module 1: Essentials of Artificial Intelligence and Machine Learning

Author: Winston Liaw, MD, MPH

Learning Objectives

Upon completing this course, the learner should be able to:

• Define key AI/ML terms such as artificial intelligence, machine learning, and neural networks
• Identify three limitations of and risks related to AI applications
• List the steps involved with developed AI/ML tools
• Describe how AI/ML tools can be used in primary care

Module 2: Social and Ethical Implications of Artificial Intelligence and Machine Learning

Author: Winston Liaw, MD, MPH

Learning Objectives

Upon completing this course, the learner should be able to:

• Describe the mechanisms through which AI/ML tools can exacerbate health disparities
• List strategies for mitigating the negative effects of bias within AI/ML tools
• Describe ethical issues relevant to AI/ML tools
• Recognize the societal consequences of AI/ML tools

Accreditation

American Academy of Family Physicians (AAFP) CME Credit

The AAFP has reviewed Artificial Intelligence and Machine learning for Primary Care, and deemed it acceptable for AAFP credit. Term of approval is from 12/13/2025 to 12/12/2026. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

This session, Module 1: Artificial Intelligence / Machine Learning (AI/ML) Essentials, is approved for up to 1.0 Enduring Materials, Self-Study AAFP Prescribed credit. 

This session, Module 2: Social and Ethical Implications of AI/ML, is approved for up to 1.0 Enduring Materials, Self-Study AAFP Prescribed credit.

AMA/AAFP Equivalency: AAFP Prescribed credit is accepted by the American Medical Association as equivalent to AMA PRA Category 1 credit(s)™ toward the AMA Physician's Recognition Award. When applying for the AMA PRA, Prescribed credit earned must be reported as Prescribed, not as Category 1. 

Certificate of Participation
Upon completing the activity requirements, learners will receive a certificate of participation. This certificate can be submitted to licensing boards or associations not preapproved for credits. For credit types not listed above, participants should follow the application process outlined by the specific organization to obtain recognition.

Release Date: December 13, 2025

Expiration Date: December 12, 2026

Target Audience:  This activity is tailored to meet the educational needs of medical students, primary care residents, and practicing primary care physicians in academic and community settings. 

Mitigation of Relevant Financial Relationships

The AAFP, in collaboration with STFM and ABFM, strictly adheres to the Accreditation Council for Continuing Medical Education's (ACCME)  Standards for Integrity and Independence in Accredited Continuing Education. In alignment with these standards, all individuals involved in the development and oversight of educational content are required to disclose any financial relationships they have had with ACCME-defined ineligible companies within the past 24 months. Ineligible companies, as defined by the ACCME, are entities primarily engaged in producing, marketing, selling, reselling, or distributing healthcare products used by or on patients. Disclosures must encompass all financial relationships, regardless of their size or perceived relevance to the educational content.

The AAFP and its collaborators are committed to ensuring that all educational activities remain free from commercial bias. For this activity, no relevant financial relationships with ineligible companies have been disclosed by any individual involved in content development or oversight.

The planners, content developers, and peer reviewers from AAFP, the accredited provider, along with its collaborating partners STFM and ABFM, have no relevant financial relationships to disclose with any ACCME-defined ineligible companies.

In the second series of interviews, four more experts draw on their experience with AI both in the clinic and behind the scenes.

Total run time: 2.5 hours

Each video includes chapters which are viewable from a button at the bottom right of the player.

Interview With an Innovator, Episode 4: Nipa Shah, MD

Nipa Shah, MD, shares her experiences integrating AI into the clinic, emphasizing the importance of equitable access, reducing clinician burnout, and addressing algorithmic bias. Dr Shah shows how AI can enhance patient care, remove language barriers, and provide additional layers of operational efficiency.

Interview With an Innovator, Episode 5: Judy Gichoya, MD, MS

Judy Gichoya, MD, MS, addresses the important role that data plays in both clinical use and research that involves AI. She shares some of the challenges of algorithmic bias, the importance of data diversity, and the need for clinician involvement in AI development. As she continues discussing ethical considerations, Dr Gichoya provides insights into data security and secure data storage.

Interview With an Innovator, Episode 6: Jenna Wiens, PhD

Jenna Wiens, PhD, talks about the evolving role of predictive AI in healthcare, highlighting its potential to personalize care, enhance clinical workflows, and improve patient outcomes. Dr Wiens also discusses challenges such as bias mitigation, model interpretability, and equitable access to AI tools. She shares insights from her research on predictive analytics and offers practical advice for integrating AI into clinical practice while maintaining trust and prioritizing safety.

Interview With an Innovator, Episode 7: Vasiliki Rahimzadeh, PhD

Vasiliki Rahimzadeh, PhD, discusses AI and data ethics in healthcare innovation. Her research focuses on the scientific and social value of health data, particularly in the context of precision medicine and AI-driven decision-making. She emphasizes patient inclusion in data usage and discusses the ethical challenges of integrating AI into healthcare systems, while advocating for physicians to make informed decisions about incorporating AI tools into their clinic.

Module 3: Evidence-Based Evaluation of AI-Based Tools

Author: Jacqueline K. Kueper, PhD

Learning Objectives

During this module, you'll learn to:

• Identify the steps involved with assessing AI/ML tools
• Describe the factors that affect the quality of the data used to develop AI/ML tools
• Define terms relevant to the evaluation of AI/ML tools
• Explain the importance of evaluating the local, ongoing performance of AI/ML tools
• Identify sources of bias throughout the AI/ML development pathway that can affect performance

Module 4: AI-Enhanced Clinical Encounters

Author: Cornelius A. James, MD

Learning Objectives

During this module, you'll learn to:

• Recognize the role of patients, clinicians, and AI/ML in clinical encounters
• Describe the process of integrating outputs from AI/ML tools into clinical decision-making
• Identify the potential benefits, risks, and uncertainties associated with using AI/ML tools to augment clinical decision-making

Module 5: Integrating AI/ML Into the Clinic

Author: Winston Liaw, MD, MPH

Learning Objectives

During this module, you'll learn to:

• Describe how team science principles can be applied to the implementation of AI/ML tools
• List the steps involved with evaluating AI/ML applications for deployment in primary care
• Explain how continuous quality improvement can enhance the implementation of AI/ML tools

Accreditation

American Academy of Family Physicians (AAFP) CME Credit

The AAFP has reviewed Artificial Intelligence and Machine learning for Primary Care, and deemed it acceptable for AAFP credit. Term of approval is from 12/13/2025 to 12/12/2026. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

This session, Module 3: Evidence-Based Evaluation of AI-Based Tools, is approved for up to 1.0 Enduring Materials, Self-Study AAFP Prescribed credit. 

This session, Module 4: AI-Enhanced Clinical Encounters, is approved for up to 1.0 Enduring Materials, Self-Study AAFP Prescribed credit.

This session, Module 5: Integrating AI/ML Into the Clinic, is approved for up to 1.0 Enduring Materials, Self-Study AAFP Prescribed credit. 

AMA/AAFP Equivalency: AAFP Prescribed credit is accepted by the American Medical Association as equivalent to AMA PRA Category 1 credit(s)™ toward the AMA Physician's Recognition Award. When applying for the AMA PRA, Prescribed credit earned must be reported as Prescribed, not as Category 1. 

Certificate of Participation
Upon completing the activity requirements, learners will receive a certificate of participation. This certificate can be submitted to licensing boards or associations not preapproved for credits. For credit types not listed above, participants should follow the application process outlined by the specific organization to obtain recognition.

Release Date: December 13, 2025

Expiration Date: December 12, 2026

Target Audience:  This activity is tailored to meet the educational needs of medical students, primary care residents, and practicing primary care physicians in academic and community settings.

Mitigation of Relevant Financial Relationships

The AAFP, in collaboration with STFM and ABFM, strictly adheres to the Accreditation Council for Continuing Medical Education's (ACCME)  Standards for Integrity and Independence in Accredited Continuing Education. In alignment with these standards, all individuals involved in the development and oversight of educational content are required to disclose any financial relationships they have had with ACCME-defined ineligible companies within the past 24 months. Ineligible companies, as defined by the ACCME, are entities primarily engaged in producing, marketing, selling, reselling, or distributing healthcare products used by or on patients. Disclosures must encompass all financial relationships, regardless of their size or perceived relevance to the educational content.

The AAFP and its collaborators are committed to ensuring that all educational activities remain free from commercial bias. For this activity, no relevant financial relationships with ineligible companies have been disclosed by any individual involved in content development or oversight.

The planners, content developers, and peer reviewers from AAFP, the accredited provider, along with its collaborating partners STFM and ABFM, have no relevant financial relationships to disclose with any ACCME-defined ineligible companies. 

The Artificial Intelligence and Machine Learning for Primary Care (AiM-PC) Curriculum for medical students, primary care residents, and practicing primary care clinicians was developed in conjunction with the Society of Teachers of Family Medicine (STFM) and American Board of Family Medicine, funded by the Gordon and Betty Moore Foundation, and supported by an advisory committee. Our team consists of educators with expertise in primary care, computer science, instructional design, and ethics. By harnessing these diverse perspectives, AiM-PC will equip learners with the skills needed to be engaged stakeholders, use AI/ML in their practice, and ensure responsible and ethical use of AI/ML.

The modules in this course include:

• Module 1: AI/ML Essentials
• Module 2: Social and Ethical Implications of AI/ML
• Module 3: Evidence-Based Evaluation of AI/ML-Based Tools
• Module 4: AI/ML-Enhanced Clinical Encounters
• Module 5: Integrating AI/ML into the Clinic

While these modules are numbered, you can take them in any order you like. We recommend you begin with the first module.

Goals of the Curriculum

Our goals for this curriculum are to:

• Equip learners with the skills necessary to be engaged stakeholders in a clinical world that includes AI/ML
• Develop and disseminate innovative health care AI/ML curricula to prepare the primary care workforce
• Create efficient and effective AI/ML curricula that can be applied across various primary care settings
• Prepare learners in primary care settings to appraise and apply literature and outputs in their clinical practice
• Ensure that all patients benefit from AI/ML-augmented clinical care

Module 6: Using AI Responsibly in Rural and Safety Net Primary Care

Authors: Trevor Crowell, BA, and Amelia Sattler, MD

Learning Objectives

Upon completing this course, the learner should be able to:

• Identify common AI use cases emerging in rural and safety net primary care settings
• Assess whether an AI tool is likely to reduce burden, improve care, and/or introduce new workflow and clinical risks
• Describe practical governance steps for vetting, selecting, piloting, monitoring, and scaling AI tools in low-resource settings

Additional Materials on AI Use in Rural and Safety Net Settings

Additional materials on AI use in rural and safety net settings appears in modules 1-5.

Topics include:

• Learner-specific guidance on the use of AI
• Discussing environmental effects and resource use with patients
• Dealing with AI-generated medical advice brought by patients
• AI Governance templates
• Assessing the burden of AI on the clinical team
• and much more