SPECIAL COMMUNICATION
Richard A. Young, MD; Carmel M Martin, MBBS, MSc, PhD, MRCGP, FRACGP, FAFPHM; Joachim P. Sturmberg, MBBS, MFM, PhD, FRACGP; Sally Hall, PhD; Andrew Bazemore, MD; Ioannis A. Kakadiaris, PhD; Steven Lin, MD
Corresponding Author: Richard A Young, MD; JPS Hospital Family Medicine Residency Program
Email: ryoung01@jpshealth.org
DOI: 10.3122/jabfm.2023.230219R1
Keywords: Artificial Intelligence, Clinical Decision-Making, Complexity Science, Information Technology, Machine Learning, Medical Informatics, Primary Care Physicians, Primary Health Care, Quality Improvement
Dates: Submitted: 06-06-2023; Revised: 08-08-2023; Accepted: 08-10-2023
FINAL PUBLICATION: |HTML| |PDF|
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 healthcare 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.