Digital Technologies Impact on Healthcare Delivery: A Systematic Review of Artificial Intelligence (AI) and Machine-Learning (ML) Adoption, Challenges, and Opportunities
Open accessAbstract
Recent significant advances in the healthcare industry due to artificial intelligence (AI) and machine learning (ML) have been shown to revolutionize healthcare delivery by improving efficiency, accuracy, and patient outcomes. However, these technologies can face significant challenges and ethical considerations. This systematic review aimed to gather and synthesize the current knowledge on the impact of AI and ML adoption in healthcare delivery, with its associated challenges and opportunities. This study adhered to the PRISMA guidelines. Articles from 2014 to 2024 were selected from various databases using specific keywords. Eligible studies were included after rigorous screening and quality assessment using checklist tools. Themes were identified through data analysis and thematic analysis. From 4981 articles screened, a data synthesis of nine eligible studies revealed themes, including productivity enhancement, improved patient care through decision support and precision medicine, legal and policy challenges, technological considerations, organizational and managerial aspects, ethical concerns, data challenges, and socioeconomic implications. There exist significant opportunities, as well as substantial challenges and ethical concerns, associated with integrating AI and ML into healthcare delivery. Implementation strategies must be carefully designed, considering technical, ethical, and social factors.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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 adoption of AI in nursing raises critical ethical, social, and educational concerns.
“Despite these promising developments, the adoption of AI in nursing raises critical ethical, social, and educational concerns.”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [10]
Social barriers including resistance to change among health care professionals and unequal access to AI technologies risk widening existing gaps in health care delivery and outcomes.
“Social barriers, including resistance to change among health care professionals and unequal access to AI technologies, risk widening existing gaps in health care delivery and outcomes [9,10].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [10]
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works
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 · see citing works
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948 · see citing works
Gattrell, W. T., Logullo, P., van Zuuren, E. J., Price, A., Hughes, E. L., Blazey, P., Winchester, C. C., Tovey, D., Goldman, K., Hungin, A. P., et al. (2024) · PLOS Medicine · doi:10.1371/journal.pmed.1004326 · Cited by 701 · see citing works
Artificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626 · see citing works
Kreimeyer, K., Foster, M., Pandey, A., Arya, N., Halford, G., Jones, S. F., Forshee, R., Walderhaug, M., & Botsis, T. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.07.012 · Cited by 616 · see citing works
Ethics and governance of trustworthy medical artificial intelligence
Zhang, J., & Zhang, Z.-M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02103-9 · Cited by 507 · cited 1× · see citing works
“However, the availability of Biobanks globally, initiated to capture and store large-scale biomedical, clinical, imaging, and genomic data, which provide a rich research resource, will go a long way in addressing some of these challenges [46].”
Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research · doi:10.2196/12802 · Cited by 346 · see citing works
Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., & de Lusignan, S. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677901 · Cited by 200 · cited 3× · see citing works
“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.”
InfluentialEthical and Legal Challenges of Artificial Intelligence-Driven Healthcare
Gerke, S., Minssen, T., & Cohen, I. G. (2020) · doi:10.2139/ssrn.3570129 · Cited by 177 · see citing works
Shinners, L., Aggar, C., Grace, S., & Smith, S. (2020) · Health Informatics Journal · doi:10.1177/1460458219874641 · Cited by 148 · see citing works
Udegbe, F. C., Ebulue, O. R., Ebulue, C. C., & Ekesiobi, C. S. (2024) · International Medical Science Research Journal · doi:10.51594/imsrj.v4i4.1052 · Cited by 103 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Role of AI in Nursing Education and Practice: Umbrella Review
El Arab, R. A., Al Moosa, O. A., Abuadas, F. H., & Somerville, J. (2025) · Journal of Medical Internet Research · doi:10.2196/69881 · Cited by 160 · see citing works
“Social barriers, including resistance to change among health care professionals and unequal access to AI technologies, risk widening existing gaps in health care delivery and outcomes [9,10].”
- Authors
- Okwor, I. A., Hitch, G., Hakkim, S., Akbar, S., Sookhoo, D., & Kainesie, J.
- Affiliations
- University of Sunderland (GB)
- Corresponding author
- Hitch, Geeta
- ORCID
- Hitch, Geeta
- Venue
- AI
- Publisher
- MDPI AG
- Published
- 2024-10-12
- Volume
- 5
- Issue
- 4
- Pages
- 1918-1941
- ISSN
- 2673-2688 (ISSN-L)
- OpenAlex
- W4403483525
- Cited by
- 32
- References
- 14
- Citations by year
- 2026: 12 · 2025: 20
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- AI (publishedVersion, cc-by)
- Sunderland Repository (University of Sunderland) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- 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 › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- artificial intelligence in healthcare, machine learning adoption, health care delivery, clinical decision support, precision medicine, AI ethics, data challenges, implementation strategies, socio-economic impacts
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
