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Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges: Primary Health Care Informatics Working Group Contribution to the Yearbook of Medical Informatics 2019
Open accessAbstract
Background: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions. Objective: To form consensus about perceptions, issues, and challenges of AI in primary care. Method: A three-round Delphi study was conducted. Round 1 explored experts’ viewpoints on AI in PHC (n=20). Round 2 rated the appropriateness of statements arising from round one (n=12). The third round was an online panel discussion of findings (n=8) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups. Results: PHC and informatics experts reported AI has potential to improve managerial and clinical decisions and processes, and this would be facilitated by common data standards. The respondents did not agree that AI applications should learn and adapt to clinician preferences or behaviour and they did not agree on the extent of AI potential for harm to patients. It was more difficult to assess the impact of AI-based applications on continuity and coordination of care. Conclusion: While the use of AI in medicine should enhance healthcare delivery, we need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective.
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“Furthermore, revealing the process of how the algorithm was developed, who was involved in the development process, whether clinicians were consulted, and how the data were processed would enable acceptability [42,43].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [43]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI has significant potential to address PHC challenges by improving clinical decisions, enhancing diagnosis accuracy, and supporting disease surveillance [27–30].”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For instance, AI can improve access to care, facilitate early disease detection, automate routine clinical workflows, and support personalized treatment plans [23, 28, 31–34].”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI contributes to timely access to evidence-based information [15, 28, 62, 69, 73, 79, 81, 89, 92, 93, 98, 100, 101, 108, 110, 111, 115, 118, 123, 138, 140, 162]”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI presents a promising opportunity to address health inequalities [16, 28, 93, 107, 109, 113, 118, 127, 145], particularly by improving access to healthcare services, enhancing early detection of diseases in underserved populations, and reducing disparities in clinical decision-making.”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Strengthening AI-driven clinical practices [28, 85, 91, 95, 98, 99, 115, 120, 148–151, 161] can improve diagnostic support, reduce errors, and optimize care pathways.”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“improving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction and prevention”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“the risk of inaccuracies and misdiagnosis [28, 68, 71, 82, 85, 87, 88, 93, 95, 99–101, 107, 115, 118, 132, 148, 149, 157] due to bias, flawed algorithms, or data inconsistencies raises concerns about patient safety and reliability, requiring cautious implementation strategies.”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
The cited source says what the claim attributes to it.
In the citing paper
“AI-driven automation can also mitigate workload pressures in healthcare [28, 62, 67, 68, 72, 73, 77, 87, 89, 92, 101, 104, 106, 112, 118–120, 135, 145, 146, 148, 159]”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [28]
In the citing paper
“It requires algorithmic validation, using techniques such as federated learning or subgroup analysis, to ensure they are controlled for bias [37,45]”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [37]
In the citing paper
“Eleven studies suggest that physicians should understand the role of data science in AI [12,13,16,37,39,41,42,45].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [37]
In the citing paper
“Nine studies discuss the need for a fundamental understanding of AI ethics to improve the adoption of AI in medicine and ensure its safe use [12,13,16,37,39,45].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [37]
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“The rapid growth of technology in health care has become a catalyst for evidence-based practice, and the integration of AI holds significant potential for improving health care service delivery [18].”
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BackgroundResultHard budget constraints and artificial intelligence technology
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Ibrahim, A. M., Zoromba, M. A., Abousoliman, A. D., Zaghamir, D. E. F., Alenezi, I. N., Elsayed, E. A. H., & Mohamed, H. A. H. (2025) · BMC Nursing · doi:10.1186/s12912-025-02798-3 · Cited by 36
US primary care in 2029: A Delphi survey on the impact of machine learning
Blease, C. R., Kharko, A., Locher, C., DesRoches, C. M., & Mandl, K. D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239947 · Cited by 29
Henckert, D., Malorgio, A., Schweiger, G., Raimann, F. J., Piekarski, F., Zacharowski, K. D., Hottenrott, S., Meybohm, P., Tscholl, D. W., Spahn, D. R., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12062096 · Cited by 26
Tursynbek, A., Zhaksylykova, D., Cruz, J. P., & Balay‐odao, E. M. (2024) · Journal of Clinical Nursing · doi:10.1111/jocn.17584 · Cited by 32
Oremulé, B., Saunders, G. H., Kluk, K., d’Elia, A., & Bruce, I. A. (2024) · The Journal of Laryngology & Otology · doi:10.1017/s0022215124000550 · Cited by 24
Wewetzer, L., Held, L. A., Goetz, K., & Steinhäuser, J. (2023) · DIGITAL HEALTH · doi:10.1177/20552076231176644 · Cited by 23
Abdulazeem, H. M., Meckawy, R., Schwarz, S., Novillo-Ortiz, D., & Klug, S. J. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105945 · Cited by 33
Madi, M., Nielsen, S., Schweitzer, M., Siebert, M., Körner, D., Langensiepen, S., Stephan, A., & Meyer, G. (2024) · BMC Nursing · doi:10.1186/s12912-024-01849-5 · Cited by 23
“The end-users’ acceptance is a core concept in the adoption, implementation and evaluation of new systems in daily healthcare and nursing practice [9, 10].”
BackgroundMahl, D., Schäfer, M. S., Voinea, Ş. A., Adib, K., Duncan, B., Salvi, C., & Novillo-Ortiz, D. (2025) · BMJ Global Health · doi:10.1136/bmjgh-2024-018545 · Cited by 24
“45 It has also been used to examine the role of AI in emergency medicine, 46 mental health, 47 primary care 48 and surgery.”
History, current status, and future directions of artificial intelligence
Kubassova, O., Shaikh, F., Melus, C., & Mähler, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820239-5.00002-4 · Cited by 25
Artificial intelligence: a new cutting-edge tool in spine surgery
Kalanchiam, G. P., Chandramohan, T., Raman, M., & Kalyanasundaram, H. (2024) · Asian Spine Journal · doi:10.31616/asj.2023.0382 · Cited by 22
Ghadiri, P., Yaffe⃰, M. J., Adams, A. M., & Rahimi, S. A. (2024) · BMC Family Practice · doi:10.1186/s12875-024-02417-1 · Cited by 24
“It has the potential to improve healthcare services [31], including automating medical devices [30], administrative planning [32], and resource management [33] to support prevention, screening, diagnostics, and treatment [30].”
BackgroundMethodRony, M. K. K., Akter, K., Debnath, M., Rahman, M. M., Johra, F. T., Akter, F., Das, D. C., Mondal, S., Das, M., Uddin, M. J., et al. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100113 · Cited by 28
Forecasting the future of smart hospitals: findings from a real-time delphi study
Jovy-Klein, F., Stead, S., Salge, T. O., Sander, J., Diehl, A., & Antons, D. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11895-z · Cited by 27
Bhatt, S. A., Cohon, A., Rose, J., Majerczyk, N., Cozzi, B., Crenshaw, D., & Myers, G. (2021) · NEJM Catalyst · doi:10.1056/cat.21.0008 · Cited by 20
GÜLTEKİN, M., & ŞAHİN, M. (2024) · Cyberpsychology Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2024-1-6 · Cited by 23
Jeong, K., Mallard, A. R., Coombe, L. L., & Ward, J. S. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102534 · Cited by 19
Artificial intelligence in emergency medicine: a primer for the nonexpert
Smith, M. E., Zalesky, C. C., Lee, S., Gottlieb, M., Adhikari, S., Goebel, M., Wegman, M., Garg, N., & Lam, S. H. F. (2025) · JACEP Open · doi:10.1016/j.acepjo.2025.100051 · Cited by 18
Assessing the impact of AI on physician decision-making for mental health treatment in primary care
Ryan, K., Yang, H., Kim, B., & Kim, J. P. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00124-y · Cited by 17
Artificial intelligence for family medicine research in Canada: current state and future directions
Kueper, J. K., Emu, M., Banbury, M., Bjerre, L. M., Choudhury, S., Green, M., Pimlott, N., Slade, S., Tsuei, S. H., & Sisler, J. (2024) · Canadian Family Physician · doi:10.46747/cfp.7003161 · Cited by 16
Goetz, C. M., Arnetz, J. E., Sudan, S., & Arnetz, B. B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243641 · Cited by 17
“Likewise, primary health care providers and health informatics experts shave been shown to hold similar concerns about AI systems in patient care [36].”
BackgroundMehraeen, E., Siami, H., Montazeryan, S., Molavi, R., Feyzabadi, A., Parvizy, I., Masjedlu, Z. A., Dehkalani, M. N., Mahmoudi, S., & Ahmadipour, A. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.70017 · Cited by 14
Gebrye, T., Fatoye, F., Mbada, C. E., & Hakimi, Z. (2023) · Rheumatology International · doi:10.1007/s00296-023-05354-x · Cited by 15
“Given the importance of RWD in research, population health, quality improvement, clinical decision support, and personalised medicine [21], it is necessary to explore the existing QA tools that have been used for SRs and MAs that involved RWD.”
BackgroundGomez-Cabello, C. A., Prabha, S., Haider, S. A., Genovese, A., Collaço, B. G., Wood, N. G., Bagaria, S. P., & Forte, A. J. (2025) · Bioengineering · doi:10.3390/bioengineering12111194 · Cited by 17
Artificial Intelligence in Digital Health: Issues and Dimensions of Ethical Concerns
Ishengoma, F. (2022) · Innovación y Software · doi:10.48168/innosoft.s8.a48 · Cited by 10
“Thus, as healthcare workers are extensively using AI it impede the development of doctors' abilities and clinical procedures [36].”
BackgroundArtificial Intelligence (AI) and the future of Iran’s Primary Health Care (PHC) system
Dehnavieh, R., Inayatullah, S., Yousefi, F., & Nadali, M. (2025) · BMC Primary Care · doi:10.1186/s12875-025-02773-6 · Cited by 13
“As mentioned, AI represents a significant technological advancement that carries extensive and rapid implications for PHC systems worldwide [26, 39]. its increasing relevance, many PHC systems, including those in Iran, have encountered difficulties in effectively responding to these transformative…”
Barriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review
Assadullah, M. M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3530598 · Cited by 13
Hunik, L., Chaabouni, A., van Laarhoven, T., Hartman, T. O., Leijenaar, R. T. H., Cals, J., Uijen, A. A., & Schers, H. (2025) · JMIR Medical Informatics · doi:10.2196/62862 · Cited by 14
“AI-based prediction models could aid in decision-making for faster and more accurate diagnoses, with more diagnostic efficiency that can benefit patients’ health [5-8].”
Yang, M., Yan, Y., Zhong, X., Liu, H., Jing, R., Zheng, Y., Cai, Z., Liu, Z., & Gong, K. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12787-6 · Cited by 14
“The counselees should be informed of medical risks and recommended to offline visits if their consultations were judged as first visits [50].”
Rice, E. J., Mashford‐Pringle, A., Qiang, J. R., Henderson, L., MacLean, T., Rhoden, J., Simms, A. J., & Stutz, S. (2023) · Learning Health Systems · doi:10.1002/lrh2.10376 · Cited by 11
AI-Powered Precision Oncology: Computational Insights Redefining Therapeutic Landscapes
Tippur, A. (2023) · DHR Proceedings · doi:10.47488/dhrp.v3is1.95 · Cited by 12
Breitwieser, M., Zirknitzer, S., Poslusny, K., Freude, T., Scholsching, J., Bodenschatz, K. J., Wagner, A., Hergan, K., Schaffert, M., Metzger, R. P., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15162117 · Cited by 10
Chaparala, S. P., Pathak, K. D., Dugyala, R. R., Thomas, J. G., & Varakala, S. P. (2025) · Cureus · doi:10.7759/cureus.77758 · Cited by 10
“This happens because AI learns from already published data [24].”
Implementing Artificial Intelligence in Family Medicine: Challenges and Limitations
Katsakiori, P. F., Kagadis, G. C., Mulita, F., & Marangos, M. N. (2024) · Cureus · doi:10.7759/cureus.75518 · Cited by 13
“However, assessing the impact of AI applications on continuity and coordination of care may be difficult [23].”
InfluentialSuha, K. T., Lubenow, H., Soria-Zurita, S., Haw, M. P., Vettukattil, J. J., & Jiang, J. (2025) · Medicina · doi:10.3390/medicina61040561 · Cited by 12
“ML techniques, including DL in medicine, are heavily criticized for their “black box” nature since the way in which many ML models arrive at decisions cannot be explained, and the decision process is not transparent [58,59].”
Halk Sağlığında Yapay Zekanın Kullanımı
Alıcılar, H. E., & Çöl, M. (2021) · Uludağ Üniversitesi Tıp Fakültesi Dergisi · doi:10.32708/uutfd.891274 · Cited by 9
“Aynı zamanda yapay zeka sistemleri bu potansiyel soruna çözüm olabilecek şekilde en çok risk altında olan bireyleri ortaya çıkararak sağlık eşitsizliklerini azalt-ma yönünde de yardımcı olabilir 39 .”
Cognitive Computing, Emotional Intelligence, and Artificial Intelligence in Healthcare
Alloghani, M. A., Thron, C., & Subair, S. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-92245-0_5 · Cited by 9
Artificial Intelligence in Healthcare: Foundations, Opportunities and Challenges
Glauner, P. (2021) · Future of business and finance · doi:10.1007/978-3-030-65896-0_1 · Cited by 11
Public health practice and artificial intelligence: views of future professionals
Mwase, N. S., Patrick, S. M., Wolvaardt, L., van Wyk, M., Junger, W. L., & Wichmann, J. (2023) · Journal of Public Health · doi:10.1007/s10389-023-02127-5 · Cited by 8
“The extent and implementation of AI and ML in epidemiology, in an African context, is in the early stages leaving room for exploration (Liyanage et al. 2019).”
The wizard of artificial intelligence: Are physicians prepared?
Radhwi, O., & Khafaji, M. A. (2024) · Journal of Family and Community Medicine · doi:10.4103/jfcm.jfcm_144_24 · Cited by 10
Ethical Considerations Surrounding the Use of AI-Interventions in Application of Mental Healthcare
Verma, M., Chanthar, K. M. M. V., & Beg, M. J. (2025) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3112-5.ch009 · Cited by 9
Duarte, E. S., de Moura, F. S., de Oliveira, L. P., & Garcia, L. F. (2023) · Revista de Bioética y Derecho · doi:10.1344/rbd2023.57.35146 · Cited by 10
Lin, G. S. S., Tan, W. W., & Hashim, H. (2024) · BDJ · doi:10.1038/s41415-024-7184-3 · Cited by 10
An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
Ciaburro, G. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00002-1 · Cited by 8
Bottino, L., Settino, M., & Cannataro, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70310-2_2 · Cited by 9
Shi, H., Huang, J., Jin, Y., & Han, M. (2026) · Journal of Medical Internet Research · doi:10.2196/80581 · Cited by 9
“Yet, for enhancing patient outcomes and experiences throughout the health care journey in the long term, acceptance remains a crucial factor in adopting and integrating newly introduced innovations such as AI into daily practice [7].”
Artificial Intelligence, Machine Learning, and Natural Language Processing
Shoenbill, K. A., Kasthurirathne, S. N., & Mendonça, E. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-29171-5_36 · Cited by 9
Christopher, C., Pathak, N., Babar, Z.-U.-D., & Shrestha, S. (2025) · Aging Medicine · doi:10.1002/agm2.70051 · Cited by 5
“However, the literature also highlights that such increasing data might create dependence solely on AI applications, thus altering the patient-clinician dynamics [11].”
Metadata
- Authors
- Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., & de Lusignan, S.
- Affiliations
- University of Surrey (GB)
- UNSW Sydney (AU)
- Geisinger Holy Spirit, Camp Hill, PA, USA
- University of Ottawa (CA)
- Western University (CA)
- Department of Clinical & Experimental Medicine, University of Surrey, Guildford, Surrey, UK
- Corresponding author
- Liyanage, Harshana
- ORCID
- Venue
- Yearbook of Medical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2019-04-25
- Volume
- 28
- Issue
- 01
- Pages
- 041-046
- ISSN
- 0943-4747 (ISSN-L)
- 2364-0502
- PMC
- PMC6697547
- OpenAlex
- W2940867124
- Cited by
- 200
- References
- 19
- Citations by year
- 2026: 18 · 2025: 49 · 2024: 47 · 2023: 31 · 2022: 22 · 2021: 17 · 2020: 14 · 2019: 1
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Yearbook of Medical Informatics (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- artificial intelligence, primary care, clinical decision support, AI ethics, patient safety, continuity of care, care coordination, clinician preferences, expert consensus, AI harms
- MeSH terms
- Learning Health System, Artificial Intelligence, Delphi Technique, Primary Health Care
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