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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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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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