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Research ArticleDepartmentsF

Bridging the Gap: Transforming Primary Care Through the Artificial Intelligence and Machine Learning for Primary Care (AIM-PC) Curriculum

Winston Liaw, Brian Hischier, Cornelius A. James, Ioannis Kakadiaris, Jacqueline K. Kueper and Vasiliki Rahimzadeh
The Annals of Family Medicine November 2024, 22 (6) 570-571; DOI: https://doi.org/10.1370/afm.240537
Winston Liaw
University of Houston, Tilman J. Fertitta Family College of Medicine, Department of Health Systems and Population Health Sciences.
MD, MPH
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Brian Hischier
Society of Teachers of Family Medicine
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Cornelius A. James
University of Michigan Medical School, Department of Internal Medicine, Department of Pediatrics, Department of Learning Health Sciences
MD
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Ioannis Kakadiaris
University of Houston, Department of Computer Science
PhD
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Jacqueline K. Kueper
Scripps Research Translational Institute, Scripps Research
PhD
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Vasiliki Rahimzadeh
Baylor College of Medicine, Center for Medical Ethics and Health Policy
PhD
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Artificial intelligence and machine learning (AI/ML) are transforming primary care, and learners want to participate in the revolution. Despite beliefs that AI/ML should be a part of their training, many medical students report that this content is missing.1-3 In the absence of curricula, learners at all levels of training may be neither prepared to use AI/ML tools nor contribute to their development. They may be unfamiliar with the benefits and strengths of AI/ML, as well as its limitations, impact on health equity, and potential harm to patients. Learners may not have the skills and confidence to adopt safe and effective tools and communicate with patients about the role of AI/ML in their care. They may be unable to advocate for the financial resources needed to purchase the equipment and develop the technical expertise required to select, install, and monitor these tools. The consequences of this gap are already apparent, with only 38% of physicians using AI/ML in practice.4 In primary care specifically, research on AI/ML lags behind other specialties, and the percentage of AI/ML devices intended for use in primary care and approved by the US Food and Drug Administration is in the single digits.5,6 Reversing these trends requires the creation of a primary care workforce with the knowledge and skills needed to advance AI/ML in the specialty. While curricula to train the health care workforce exist, none focus specifically on primary care learners in the United States.7,8

To address this need, we developed the Artificial Intelligence and Machine Learning for Primary Care (AiM-PC) curriculum for medical students, primary care residents, and practicing primary care clinicians. In conjunction with the Society of Teachers of Family Medicine 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.9,10 The curriculum consists of 5 modules (AI/ML Essentials; Social and Ethical Implications of AI/ML; Evidence-Based Evaluation of AI/ML-Based Tools; AI/ML-Enhanced Clinical Encounters; and Integrating AI/ML into the Clinic) and is designed for learners with minimal experience with AI/ML. Alongside the curriculum, we are launching a video series called Interview with an Innovator. The interviewees include health care AI/ML thought leaders, all of whom provide real-world examples and context to supplement the material in the modules.

To achieve our goals, we need engagement from primary care educators and practicing clinicians. AiM-PC will be launched in a staged fashion beginning late 2024 through Spring 2025, and we are recruiting sites interested in piloting the curriculum. If interested, please contact the authors. While this training overlaps with evidence-based medicine and health informatics, we want to learn from educators and potential end-users about how this content can be integrated in educational and clinical settings. Implementation of the curriculum will be key to bridging the existing training gap and ensuring that primary care learners use AI/ML to enhance effectiveness, efficiency, and equity.

  • © 2024 Annals of Family Medicine, Inc.

References

  1. 1.↵
    1. Pinto Dos Santos D,
    2. Giese D,
    3. Brodehl S, et al.
    Medical students’ attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019; 29(4): 1640-1646. doi:10.1007/s00330-018-5601-1
    OpenUrlCrossRefPubMed
  2. 2.
    1. Blease C,
    2. Kharko A,
    3. Bernstein M, et al.
    Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland. BMJ Health Care Inform. 2022; 29(1): e100480. doi:10.1136/bmjhci-2021-100480
    OpenUrlFREE Full Text
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    1. Pucchio A,
    2. Rathagirishnan R,
    3. Caton N, et al.
    Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study. BMC Med Educ. 2022; 22(1): 815. doi:10.1186/s12909-022-03896-5
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. American Medical Association
    . AMA augmented intelligence research. Published 2023. Accessed Sep 10, 2024. https://www.ama-assn.org/system/files/physician-ai-sentiment-report.pdf
  5. 5.↵
    1. Liaw W,
    2. Kakadiaris IA.
    Artificial intelligence and family medicine: better together. Fam Med. 2020; 52(1): 8-10. doi:10.22454/FamMed.2020.881454
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Food and Drug Administration
    . Artificial intelligence and machine learning (AI/ML)-enabled medical devices. Published Aug 7, 2024. Accessed Sep 10, 2024. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
  7. 7.↵
    1. The College of Family Physicians of Canada
    . AI for family medicine. Published 2023. Accessed Sep 10, 2024. https://cfpclearn.ca/ai-for-family-medicine/
  8. 8.↵
    1. American Medical Association
    . Introduction to artificial intelligence (AI) in health care. Published Oct 31, 2023. Accessed Sep 10, 2024. https://edhub.ama-assn.org/change-med-ed/interactive/18827029
  9. 9.↵
    1. Society of Teachers of Family Medicine
    . Artificial Intelligence and Machine Learning for Primary Care Curriculum. Accessed Nov 8, 2024. https://stfm.org/teachingresources/curriculum/aim-pc/aiml_curriculum/
  10. 10.↵
    1. Liaw W,
    2. Kueper JK,
    3. Lin S,
    4. Bazemore A,
    5. Kakadiaris I.
    Competencies for the Use of Artificial Intelligence in Primary Care. Ann Fam Med. 2022;20(6):559-563. doi:10.1370/afm.2887
    OpenUrlAbstract/FREE Full Text
  11. 11.
    1. Russell RG,
    2. Lovett Novak L,
    3. Patel M, et al.
    Competencies for the Use of Artificial Intelligence–Based Tools by Health Care Professionals. Acad Med. 2023;98(3):348-356. doi:10.1097/ACM.0000000000004963
    OpenUrlCrossRefPubMed
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The Annals of Family Medicine: 22 (6)
The Annals of Family Medicine: 22 (6)
Vol. 22, Issue 6
November/December 2024
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Bridging the Gap: Transforming Primary Care Through the Artificial Intelligence and Machine Learning for Primary Care (AIM-PC) Curriculum
Winston Liaw, Brian Hischier, Cornelius A. James, Ioannis Kakadiaris, Jacqueline K. Kueper, Vasiliki Rahimzadeh
The Annals of Family Medicine Nov 2024, 22 (6) 570-571; DOI: 10.1370/afm.240537

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Bridging the Gap: Transforming Primary Care Through the Artificial Intelligence and Machine Learning for Primary Care (AIM-PC) Curriculum
Winston Liaw, Brian Hischier, Cornelius A. James, Ioannis Kakadiaris, Jacqueline K. Kueper, Vasiliki Rahimzadeh
The Annals of Family Medicine Nov 2024, 22 (6) 570-571; DOI: 10.1370/afm.240537
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