Artificial intelligence in primary care in Poland: opportunities, challenges, and public health policy implications
DOI:
https://doi.org/10.12923/2083-4829/2026-0012Keywords:
artificial intelligence, primary care, digital health, health policy, Poland, health system innovationAbstract
Introduction and Aim. Artificial intelligence (AI) is increasingly integrated into healthcare systems, yet its implementation in primary care remains uneven and context-dependent. Poland’s evolving digital health infrastructure creates favourable conditions for AI adoption in primary care (POZ), but real-world implementation remains limited. This review aims to synthesise recent international evidence with a specifi c focus on the Polish primary healthcare system.
Current knowledge. AI applications in primary care include clinical decision support systems, predictive analytics, documentation automation, and patient self-management tools. Despite high performance in controlled settings, real-world effectiveness is constrained by workflow integration, data quality and organisational readiness.
Summary. This review synthesises the current international evidence on artificial intelligence in primary care in relation to the Polish primary healthcare system, with an emphasis on implementation determinants such as interoperability, governance, clinician training and regulatory alignment. By relating global findings to the Polish healthcare system, it complements existing systematic reviews by providing a contextual synthesis focused on implementation conditions and system-level integration.
References
1. Pruszyński J, Cianciara D, Pruszyńska I, et al. Staff shortages and inappropriate work conditions as a challenge geriatrics and contemporary healthcare service at large faces. J Educ Health Sport. 2022;12(7):136-147. https://doi.org/10.12775/JEHS.2022.12.07.014
2. Kowalewska E. Physicians and AI in healthcare: insights from a mixed-method study in Poland on adoption and challenges. Front Digit Health. 2025;7:1556921. https://doi.org/10.3389/fdgth.2025.1556921
3. World Health Organization. Global strategy on digital health 2020-2025. Geneva: WHO; 2021.
4. European Commission. Digital health in the EU: state of play. Brussels: European Commission; 2022.
5. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. https://doi.org/10.1038/s41591-018-0300-7
6. Katonai G, Arvai N, Mesko B. AI and primary care: scoping review. J Med Internet Res. 2025;27:e65950. https://doi.org/10.2196/65950
7. Yousefi F, et al. Opportunities, challenges, and requirements for AI implementation in primary health care: a systematic review. BMC Prim Care. 2025;26:196. https://doi.org/10.1186/s12875-025-02785-2
8. Sani HS, et al. Adoption of artificial intelligence in primary health care: systematic synthesis of stakeholder perspectives. BMC Prim Care. 2026;27:50. https://doi.org/10.1186/s12875-025-03157-6
9. Hippisley-Cox J, et al. Development and validation of QRISK3 risk prediction algorithms. BMJ. 2017;357:j2099. https://doi.org/10.1136/bmj.j2099
10. European Parliament. Regulation (EU) 2024/1689 (EU AI Act).
11. Sutton RT, et al. An overview of clinical decision support systems. NPJ Digit Med. 2020;3:17. https://doi.org/10.1038/s41746-020-0221-y
12. Gackowski M, Połomska K, Szczucka N, et al. Digital-based system as a tool for improving healthcare coordination. Prospect Pharm Sci. 2024;22(2):1-6. https://doi.org/10.56782/pps.174
13. Traverso A, et al. Powering responsible artificial intelligence with high-quality real-world data. NPJ Digit Med. 2026;9:6. https://doi.org/10.1038/s41746-025-02132-w
14. Younis HA, et al. A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare Diagnostics. Diagnostics (Basel). 2024;14(1):109. https://doi.org/10.3390/diagnostics14010109
15. Mundzic A, et al. Exploring Primary Care Patients’ Perspectives on Artificial Intelligence. JMIR AI. 2025;4:e72211. https://doi.org/10.2196/72211
16. Damiani G, et al. Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review. BMJ Open. 2023;13:e065301. https://doi.org/10.1136/bmjopen-2022-065301
17. Liu X, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging. Lancet Digit Health. 2019;1:e271-e297. https://doi.org/10.1016/S2589-7500(19)30123-2
18. Nair M, et al. A comprehensive overview of barriers and strategies for AI implementation in healthcare. PLoS One. 2024;19:e0305949. https://doi.org/10.1371/journal.pone.0305949
19. Obermeyer Z, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447-453. https://doi.org/10.1126/science.aax2342
20. Ghadiri P, et al. Primary care physicians’ perceptions of artificial intelligence systems in the care of adolescents’ mental health. BMC Prim Care. 2024;25:215. https://doi.org/10.1186/s12875-024-02417-1
21. Wallace W, et al. The diagnostic and triage accuracy of digital and online symptom checker tools: a systematic review. NPJ Digit Med. 2022;5:118. https://doi.org/10.1038/s41746-022-00667-w
22. Nowak K, et al. Implementation of noninvasive liver disease screening tools in primary care. Korean J Fam Med. 2025;46:381-390. https://doi.org/10.4082/kjfm.25.0144
23. Liu TL, et al. AI-powered clinical documentation and clinicians’ electronic health record experience. JAMA Netw Open. 2024;7:e2432460. https://doi.org/10.1001/jamanetworkopen.2024.32460
24. Cangelosi G, et al. Barriers and facilitators to artificial intelligence implementation in diabetes management from healthcare workers’ perspective: a scoping review. Medicina (Kaunas). 2025;61(8):1403. https://doi.org/10.3390/medicina61081403
25. Longoni C, et al. Resistance to medical artificial intelligence. J Consumer Res. 2019;46:629-650. https://doi.org/10.1093/jcr/ucz013
26. Laranjo L, et al. Artificial intelligence in primary care: innovation at a crossroads. Lancet Reg Health West Pac. 2025;100078. https://doi.org/10.1016/j.lanprc.2025.100078
27. Luke A, et al. Artificial intelligence in primary care: frameworks, challenges, and guardrails. Lancet Reg Health West Pac. 2025;100079. https://doi.org/10.1016/j.lanprc.2025.100079
28. Hwang M, Zheng Y, Cho Y, et al. AI applications for chronic condition self-management: scoping review. J Med Internet Res. 2025;27:e59632. https://doi.org/10.2196/59632
29. Kueper JK, et al. Artificial intelligence and primary care research: a scoping review. Ann Fam Med. 2020;18(3):250-258. https://doi.org/10.1370/afm.2518
30. Smola P, Młoźniak I, Wojcieszko M, et al. Attitudes toward artificial intelligence and robots in healthcare in the general population: a qualitative study. Front Digit Health. 2025;7:1458685. https://doi.org/10.3389/fdgth.2025.1458685
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Praveen Malik, Adam Bednarek, Katarzyna Bednarek (Autor)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
