The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · cited 1×
“Deep learning has demonstrated significant results in various non-health and health-related applications using computer vision and natural language processing [5, 6].”
BackgroundGulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · cited 1×
“PHC is rapidly evolving not only in terms of health policies but also technologically. diagnosis of diabetic retinopathy [9].”
Contribution of Primary Care to Health Systems and Health
STARFIELD, B., SHI, L., & MACINKO, J. (2005) · The Milbank Quarterly · doi:10.1111/j.1468-0009.2005.00409.x · Cited by 5,507 · cited 1×
“Primary health care (PHC) to some extent can respond to these demands at both population and community levels [2].”
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · cited 1×
“The rise in ageing population with multiple chronic diseases together with the increase of healthcare spending worldwide are some now possible to exploit these health information systems using Artificial intelligence (AI) concepts such as machine learning and deep learning [3, 4].”
Background(2001) · Defense Technical Information Center (DTIC) · Cited by 2,158
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · cited 1×
“An AI system can incorporate the biases inherent to the training data set, and propagate them into the validation set [12].”
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · cited 1×
“developed an AI system, approved by the Federal Drug Administration of USA, to detect diabetic retinopathy in PHC centres [8].”
BackgroundXiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · cited 1×
“The rise in ageing population with multiple chronic diseases together with the increase of healthcare spending worldwide are some now possible to exploit these health information systems using Artificial intelligence (AI) concepts such as machine learning and deep learning [3, 4].”
BackgroundTowards a comprehensive public health response to population ageing
Beard, J., & Bloom, D. E. (2014) · The Lancet · doi:10.1016/s0140-6736(14)61461-6 · Cited by 612 · cited 1×
“Yearb Med Inform 2019:41-6 http://dx.doi.org/10.1055/s-0039-1677901 of the key factors putting strain on healthcare systems [1].”
InfluentialGoogle DeepMind and healthcare in an age of algorithms
Powles, J., & Hodson, H. (2017) · Health and Technology · doi:10.1007/s12553-017-0179-1 · Cited by 511 · cited 1×
“Deep learning has demonstrated significant results in various non-health and health-related applications using computer vision and natural language processing [5, 6].”
Background
Cited by
Sorted by citations
Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00852-5 · Cited by 404
Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 435
“It is essential that as the AI system matures, it is designed such that it can “adopt and challenge contradictory rules and behaviours” [43].”
BackgroundInfluentialWith an eye to AI and autonomous diagnosis
Keane, P. A., & Topol, E. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0048-y · Cited by 304
Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study
Castagno, S., & Khalifa, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.578983 · Cited by 304
“However, such concerns are often not specific to AI or machine learning, but rather on theway they are employed and therefore other authors believe that an appropriate, informed use of AI may be beneficial and may greatly improve patient care (McDonald et al., 2017; EsteChanva et al., 2019; Liyanage et al., 2019).”
BackgroundKarimian, G., Petelos, E., & Evers, S. (2022) · AI and Ethics · doi:10.1007/s43681-021-00131-7 · Cited by 328
“The concerns regarding this principle were the lack of patient centeredness [36], the lack of shared decision making, [38] and the potential to ignore individual preferences when applying AI-driven DSS/ CDSS, as an AI-tool or algorithm may have been based on limited epidemiological evidence and may…”
BackgroundMethod
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
