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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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.
“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]
The Results section, “Ethical and Lawful Processing of Patient Data by AI Applications” (page unavailable), reports agreement on the need for transparency about data processing. It does not say that disclosing how an algorithm was developed, who developed it, or whether clinicians were consulted would increase acceptability. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI has significant potential to address PHC challenges by improving clinical decisions, enhancing diagnosis accuracy, and supporting disease surveillance.
“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]
The Conclusions and Results (‘Future of AI in Primary Care’; page not provided) support AI’s potential to improve clinical decisions, while Table 1 gives examples of diagnostic assistance and earlier diagnosis. Our copy does not establish improved diagnostic accuracy or address disease surveillance, so those parts of the attributed claim are unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI can improve access to care, facilitate early disease detection, automate routine clinical workflows, and support personalized treatment plans.
“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]
Table 1 (Methods > Consensus Exercise; page not supplied) lists improving access through triage and early diagnosis as potential AI uses in primary care. The Results > Future of AI in Primary Care discusses support for diagnostic and therapeutic decisions and background data analysis, but does not establish automation of routine clinical workflows or personalized treatment plans. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI contributes to timely access to evidence-based information.
“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]
The Results section, “Future of AI in Primary Care” (page unavailable), says AI can analyse data in the background to give providers a more integrated patient record during a consultation. That supports access to patient information at the point of care, but the paper does not say AI provides timely access to evidence-based information. (LLM, conf=0.75)
Experimental — accuracy not yet measuredAI presents a promising opportunity to address health inequalities, particularly by improving access to healthcare services, enhancing early detection of diseases in underserved populations, and reducing disparities in clinical decision-making.
“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]
The Results (Future of AI in Primary Care; page unavailable) say AI may reduce health inequalities by identifying vulnerable patients. Table 1 (Methods, Consensus Exercise; page unavailable) lists improving accessibility and early diagnosis as potential primary-care uses. Neither passage specifically connects early diagnosis to underserved populations or says AI reduces disparities in clinical decision-making. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Strengthening AI-driven clinical practices can improve diagnostic support, reduce errors, and optimize care pathways.
“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]
The Results (Future of AI in Primary Care) describe potential diagnostic decision support, and a panel statement anticipates improvements in care processes and safety. The paper does not establish that AI reduces errors; its Results (Risks Associated with Using AI in Primary Care) warn that AI can propagate prescribing errors or produce incorrect diagnoses. (LLM, conf=0.84)
Experimental — accuracy not yet measuredImproving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction and prevention are among the most significant opportunities in providing comprehensive primary care by AI.
“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]
Table 1 (Methods > Consensus Exercise; page unavailable) lists diagnostic assistance, early disease diagnosis, and prediction of patients at high risk as potential AI uses in primary care. The Future of AI in Primary Care section also discusses risk stratification. Our copy does not identify optimization of screening programs or disease prevention as opportunities, nor rank these uses among the most significant. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe risk of inaccuracies and misdiagnosis due to bias, flawed algorithms, or data inconsistencies raises concerns about patient safety and reliability, requiring cautious implementation strategies.
“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]
AI-driven automation can mitigate workload pressures in healthcare.
“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]
Algorithmic validation using techniques such as federated learning or subgroup analysis ensures AI is controlled for bias.
“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]
Eleven studies suggest that physicians should understand the role of data science in AI.
“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]
Nine studies discuss the need for a fundamental understanding of AI ethics to improve adoption of AI in medicine and ensure its safe use.
“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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Self-citationTürkiye ve Dünyada Sağlık Hizmetlerinde Yapay Zekâ
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“AI has been proven to have the ability to deliver significant benefits in a variety of sectors and levels of health care [18], including administrative planning [19], resource management [20], prevention [21], screening [22], diagnostics [23], and treatment [24,25].”
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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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InfluentialThe application of artificial intelligence in hepatology: A systematic review
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“Liyanage et al. [27] acknowledged the potential of AI to improve healthcare delivery but recommended further scrutiny of AI systems and mechanisms to detect biases in unsupervised algorithms.”
InfluentialTanaka, M., Matsumura, S., & Bito, S. (2023) · JMIR Formative Research · doi:10.2196/46020 · Cited by 30 · see citing works
“It is likely possible for AI to extract quantified preferences and values [25], but the context is distinctly different before and after a preference or value is born.”
BackgroundThe perceptions of medical physicists towards relevance and impact of artificial intelligence
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“Several studies have focused on healthcare professionals’ perception and attitude towards AI [14–20].”
BackgroundApplications and challenges of neural networks in otolaryngology (Review)
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“However, concerns were raised when the topic of unsupervised ML was raised, since it is not yet possible to use this to obtain faithful results (72).”
BackgroundResultHard budget constraints and artificial intelligence technology
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“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].”
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“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
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Artificial intelligence: a new cutting-edge tool in spine surgery
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“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].”
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Forecasting the future of smart hospitals: findings from a real-time delphi study
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Artificial intelligence in emergency medicine: a primer for the nonexpert
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Assessing the impact of AI on physician decision-making for mental health treatment in primary care
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Artificial intelligence for family medicine research in Canada: current state and future directions
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“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 · see citing works
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“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 · see citing works
Artificial Intelligence in Digital Health: Issues and Dimensions of Ethical Concerns
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“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 · see citing works
“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
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“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].”
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“The counselees should be informed of medical risks and recommended to offline visits if their consultations were judged as first visits [50].”
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AI-Powered Precision Oncology: Computational Insights Redefining Therapeutic Landscapes
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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 · see citing works
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“This happens because AI learns from already published data [24].”
Implementing Artificial Intelligence in Family Medicine: Challenges and Limitations
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“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 9 · see citing works
“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 · see citing works
“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
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Artificial Intelligence in Healthcare: Foundations, Opportunities and Challenges
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Public health practice and artificial intelligence: views of future professionals
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“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?
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Ethical Considerations Surrounding the Use of AI-Interventions in Application of Mental Healthcare
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An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm
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“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
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“However, the literature also highlights that such increasing data might create dependence solely on AI applications, thus altering the patient-clinician dynamics [11].”
- 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 · 2018: 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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