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Cited by 17Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareArtificial Intelligence Applications

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care

Open access
Young, Richard A., Martin, Carmel M., Sturmberg, Joachim P., Hall, Sally, Bazemore, Andrew, Kakadiaris, Ioannis A., Lin, Steven·2024·The Journal of the American Board of Family Medicine
DOI 10.3122/jabfm.2023.230219r1
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Abstract

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

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  • To date, most AI CDM tools have been focused on specific organ or disease problems because of the narrower data elements needed for the LLMs compared to the undifferentiated nature of PCC patients.

    “Despite these knowledge differences, to date, most AI CDM tools have been focused on specific organ or disease problems because of the narrower data elements (i.e., liver enzymes and imaging) needed for the LLMs compared to the undifferentiated nature of PCC patients. 2”

    From The use of an artificial intelligence platform OpenEvidence to augment clinical decision-making for primary care physicians (2025) · cites this paper as [2]

    The Background section describes successful AI/ML applications for detecting specific diseases, while the Abstract and Discussion say tools are more likely to work on narrow, well-defined tasks than in complex primary care decision making (page numbers unavailable). The paper does not establish that most clinical decision-making tools target organ- or disease-specific problems, or that LLMs require narrower data elements such as liver enzymes and imaging. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • AI utilization in primary care clinics is in the early stages of development, with most studies focusing on AI methods to support physician clinical decision-making with diagnostic and treatment recommendations, particularly for chronic conditions.

    “A recent scoping review found that AI utilization in primary care clinics (PCCs) is in the early stages of development, with most studies focusing on AI methods to support physician clinical decision-making (CDM) with diagnostic and treatment recommendations, particularly with management of chronic conditions.1,2”

    From The use of an artificial intelligence platform OpenEvidence to augment clinical decision-making for primary care physicians (2025) · cites this paper as [2]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

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Authors
Young, R. A., Martin, C. M., Sturmberg, J. P., Hall, S., Bazemore, A., Kakadiaris, I. A., & Lin, S.
Affiliations
Australian National University (AU)
ORCID
Venue
The Journal of the American Board of Family Medicine
Publisher
American Board of Family Medicine (ABFM)
Published
2024-03
Volume
37
Issue
2
Pages
332-345
ISSN
  • 1557-2625 (ISSN-L)
  • 1558-7118
DOI
10.3122/jabfm.2023.230219r1
OpenAlex
W4396851668
Cited by
17
References
72
Citations by year
2026: 3 · 2025: 11 · 2024: 3
FWCI
1.61
Type
journal article
Language
en
Open access
Locations
  • The Journal of the American Board of Family Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • NOVA (University of Newcastle Australia) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence, machine learning, primary care, complexity science, complex adaptive systems, clinical decision-making, physician-patient relationship, shared decision-making, comorbidities, electronic health records, AI implementation, clinical workflow integration
MeSH terms
Machine Learning, Artificial Intelligence, Humans, Physician-Patient Relations, Primary Health Care, Electronic Health Records, Quality Improvement

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