Keywords
Artificial intelligence
Intelligent systems
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Abstract
Objective: To analyze applications, user perceptions, implementation barriers, and ethical implications of Artificial Intelligence (AI) in Primary Health Care (PHC), proposing strategies for safe and equitable integration. Methods: Integrative review with data collection from scientific databases (2020-2025). The study object comprises global scientific production on AI in PHC, utilizing a structured protocol with controlled descriptors (MeSH/DeCS) and thematic inclusion criteria. Results: Results reveal a maturity paradox: while fields such as ophthalmology and dermatology achieve advanced technical precision, clinical integration remains fragmented by "contextual specialization." The Global North focuses on administrative efficiency, whereas the Global South employs AI as an access equalizer via technological leapfrogging. A "cautious optimism" was identified: professionals face the dilemma of deskilling and the dehumanization of the therapeutic bond, while patients prioritize diagnostic speed. Interoperability, data quality, and governance emerge as critical bottlenecks hindering the definitive transition from in silico validation to real-world application. Final considerations: The study's strength is its unique multidimensional synthesis, integrating technical evidence with PHC-specific sociocultural and ethical dimensions. The limitation lies in the exclusion of proprietary innovations under institutional paywalls.
References
BARRY B, et al. Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study. JMIR AI, 2022; 1(1): e41940.
BORGES DGF, et al. Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records. BMC Medical Research Methodology, 2025; 25(1): 99.
BRAUN V, CLARKE, V. Successful Qualitative Research a practical guide for beginners. 1st ed. SAGE: Los Angeles – LA, London – LCY, New Delhi, NCT, Singapore, SGP e Washington, DC, 2013; p173.
BRESSAN T, et al. AI scribes in rural and remote primary care: an antidote to physician burnout or Pandora’s Box? Rural and Remote Health, 2025; 25: 9430.
CATALINA QM, et al. Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool. DIGITAL HEALTH, 2023; 9: 20552076231180511.
COOPER J, et al. Perspectives of Health Care Professionals on the Use of AI to Support Clinical Decision-Making in the Management of Multiple Long-Term Conditions: Interview Study. Journal of Medical Internet Research, 2025; 27: e71980.
DARCEL K, et al. Implementing artificial intelligence in Canadian primary care: Barriers and strategies identified through a national deliberative dialogue. PLOS ONE, 2023; 18(2): e0281733.
DE OLIVEIRA EL, DE FARIAS MC. TWINVAX: conceptual model of a digital twin for immunisation services in primary health care. Frontiers in Public Health, 2025; (13): 1568123.
DORMOSH N, et al. Topic evolution before fall incidents in new fallers through natural language processing of general practitioners’ clinical notes. Age and Ageing, 2024; 53(2): p. afae016.
FERRIS LK, et al. DERM-SUCCESS FDA Pivotal Study: A Multi-Reader Multi-Case Evaluation of Primary Care Physicians’ Skin Cancer Detection Using AI-Enabled Elastic Scattering Spectroscopy. Journal of primary care & community health, 2025; 16: 1–9.
GARIES S, et al. Artificial intelligence in primary care practice: Qualitative study to understand perspectives on using AI to derive patient social data. Canadian Family Physician, 2024; 70(7–8): e102–e109.
GIAVINA-BIANCHI M, et al. Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting. PLOS ONE, 2021; 16(9): e0257006.
GOLDBERG DG, et al. Clinicians’ perspectives on the adoption and implementation of EMR-integrated clinical decision support tools in primary care. DIGITAL HEALTH, 2025; (11): 20552076251334043.
JI L, et al. Performance of a Full-Coverage Cervical Cancer Screening Program Using on an Artificial Intelligence– and Cloud-Based Diagnostic System: Observational Study of an Ultralarge Population. Journal of Medical Internet Research, 2024; 26: e51477.
JONES OT, et al. User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study. JMIR Cancer, 2025; 11: e60653.
KARAMI M, MADLOOL H. Artificial intelligence and digital health in the health systems of developing countries: the challenges and vision of integration in the primary health care setting. Frontiers in Digital Health, 2025; 7: 1532361.
KHAN, H, BOKHARI SFH. Integrating Artificial Intelligence (AI) Chatbots for Depression Management: A New Frontier in Primary Care. Cureus, 2024; 16(8): e66857
KUEPER JK, et al. Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation. BMJ Health & Care Informatics, 2022; 29(1): e100493.
LI J, et al. Integrated image-based deep learning and language models for primary diabetes care. Nature Medicine, 2024; 30(10): 2886–2896.
MAYER MA. Inteligencia artificial en atención primaria: un escenario de oportunidades y desafíos. Atención Primaria, 2023; 55(11): 102744.
MILLER IJ, et al. Implementation of artificial intelligence for the detection of cutaneous melanoma within a primary care setting: prevalence and types of skin cancer in outdoor enthusiasts. PeerJ,2023; 11: e15737.
MOSCHOGIANIS S, et al. Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views. The Annals of Family Medicine, 2025; 23(3): 214–222.
NASH DM, et al. Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres. The Journal of the American Board of Family Medicine, 2023; 36(2): 221–228.
NOLAN BM, et al. Patient and Provider Experience with Artificial Intelligence Screening Technology for Diabetic Retinopathy in a Rural Primary Care Setting. Journal of Maine Medical Center, 2023; 5(2): doi:10.46804/2641-2225.1144
PARHAM GP, et al. Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI)-based automated visual evaluation. Infectious Agents and Cancer, 2023; 18(1): 61.
RAZAI MS, et al. Implementation challenges of artificial intelligence (AI) in primary care: Perspectives of general practitioners in London UK. PLOS ONE, 2024; 19(11): e0314196.
RUAMVIBOONSUK P, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. The Lancet Digital Health, 2022; 4 (4): e235–e244.
SIIRA E, et al. Healthcare leaders’ experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study. BMC Primary Care, 2024; 25(1): 268.
SMOKOVSKI I, et al. Digital biomarkers: 3PM approach revolutionizing chronic disease management — EPMA 2024 position. EPMA Journal, 2024; 15(2): 149–162.
SOUZA MT, et al. Integrative review: what is it? How to do it? Einstein, 2010; 8 (1): 102–106.
STEERLING E, et al. Influences on trust in the use of AI-based triage—an interview study with primary healthcare professionals and patients in Sweden. Frontiers in Digital Health, 2025; 7: 1565080.
TAN NC, et al. Supporting quadruple aim in primary care using artificial intelligence. Annals of the Academy of Medicine, Singapore, 2025; 54(5): 305–309.
TERRY AL, et al. Is primary health care ready for artificial intelligence? What do primary health care stakeholders say? BMC Medical Informatics and Decision Making, 2022; 22(1): 237.
TRONCOSO EL. The Greatest Challenge to Using AI/ML for Primary Health Care: Mindset or Datasets? Frontiers in Artificial Intelligence, 2020; 3: 53.
UPSHAW TL, et al. Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders. The Journal of the American Board of Family Medicine, 2023; 36 (2): 210–220.
VALLÈS-PERIS N, PARETO J. Artificial intelligence as a mode of ordering. Automated-decision making in primary care. Information, Communication & Society, 2025; 28(11): 2015–2033.
VIDAL-ALABALL J, et al. The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities. Atención Primaria, 2024; 56(2): 102820.
VORDENBERG SE, et al. Investigating Older Adults’ Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey. Journal of Medical Internet Research, 2024; 26: e60794.
WÄNDELL P, et al. A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care. ESC Heart Failure, 2025; 12(1): 613–621.
YOUNG RA, et al. What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care. The Journal of the American Board of Family Medicine, 2024; 379(2): 332–345.