Artificial intelligence in healthcare: opportunities and risk for future
Open accessAbstract
OBJECTIVE: This study aimed to find out the opportunity of artificial intelligence (AI) and the risk in health service. METHOD: A comprehensive literature search was collected from three databases (Web of Science, Google Scholar, and EBSCOhost) to identify articles studied Implementing AI in improving in health services. Two reviewers independently assessed the quality of studies using the Joanna Briggs Institute. RESULTS: The application will improve diagnostics, prevention, and treatment of patients, increasing cost efficiency and equality and equality in health services. For the challenge, there is no AI adoption in public sector, patients' privacy, patient autonomy rights become problems in AI applications. CONCLUSIONS: Implementation of AI is needed in the efficiency of health service management as well as making medical decisions. The challenge is facilitating early adoption and ongoing implementation in the health care system, and we consider some of the ethical problem lists faced by AI clinical application.
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Inaccurate and underrepresentative training data sets for AI models can cause bias, misleading predictions, adverse events, and large-scale discrimination, causing barriers to adoption.
“Inaccurate and underrepresentative training data sets for AI models can cause bias, misleading predictions, adverse events, and large-scale discrimination, causing barriers to adoption [32,41,47,48].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [47]
The Equity section (page unavailable) explicitly says inaccurate and underrepresentative training data can cause bias, misleading predictions, adverse events, and large-scale discrimination. The Challenges and benefits section identifies data bias as an application problem and early adoption as a challenge, but does not explicitly say those data-related harms cause barriers to adoption. (LLM, conf=0.83)
Experimental — accuracy not yet measuredLack of accuracy, doubts about unsafe results, privacy breaches, patients' perceptions and acceptance of automation, and uncertainty about developers' reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust.
“Lack of accuracy; doubts about unsafe results; privacy breaches; patients’ perceptions and acceptance of automation; and uncertainty about developers’ reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust [39,42,47,50,53,54,69,70].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [47]
The Equity section links potentially unsafe AI results to an obstacle for doctors accepting AI, and the Introduction and Challenges and benefits sections discuss system errors and privacy risks. The paper also identifies lack of consideration for users’ perspectives as an adoption challenge, but these passages do not establish patients’ acceptance of automation or uncertainty about developers’ reliability, availability, usability, and perceived usefulness as obstacles to trust. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrivacy and ethics concerns were focused on maintaining confidentiality, ensuring processes are in place to obtain consent, and having informed consent with clarity on how data will be processed.
“Concerns about privacy and ethics were focused on maintaining confidentiality, ensuring processes are in place to obtain consent, and having informed consent with clarity on how the data will be processed [32,39,47,56].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [47]
The “2) Personal” section calls for patient confidentiality and adequate processes for obtaining consent. The copy does not specify that informed consent must clarify how data will be processed; the transparency discussion concerns explaining clinical decisions, not data processing. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Cites
Reprint—Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & and the PRISMA Group (2009) · Physical Therapy · doi:10.1093/ptj/89.9.873 · Cited by 2,486 · see citing works
Big data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works
A governance model for the application of AI in health care
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz192 · Cited by 979 · 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
Digital transformation in healthcare – architectures of present and future information technologies
Gopal, G., Suter‐Crazzolara, C., Toldo, L., & Eberhardt, W. (2018) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2018-0658 · Cited by 396 · see citing works
Trends in IoT based solutions for health care: Moving AI to the edge
Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.016 · Cited by 318 · see citing works
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature
Triantafyllidis, A. K., & Tsanas, A. (2019) · Journal of Medical Internet Research · doi:10.2196/12286 · Cited by 243 · see citing works
Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
Tobore, I., Li, J., Yuhang, L., Al-Handarish, Y., Kandwal, A., Nie, Z., & Wang, L. (2019) · JMIR mHealth and uHealth · doi:10.2196/11966 · Cited by 180 · see citing works
Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance
Guan, J. (2019) · Chinese Medical Sciences Journal · doi:10.24920/003611 · Cited by 170 · see citing works
Health Care, Capabilities, and AI Assistive Technologies
Coeckelbergh, M. (2009) · Ethical Theory and Moral Practice · doi:10.1007/s10677-009-9186-2 · Cited by 167 · see citing works
Regulatory alternatives for AI
Clarke, R. (2019) · Computer law & security review · doi:10.1016/j.clsr.2019.04.008 · Cited by 104 · see citing works
Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment
Pee, L. G., Pan, S. L., & Cui, L. (2018) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24145 · Cited by 78 · see citing works
Akmal, A., Greatbanks, R., & Foote, J. L. (2020) · Health Policy · doi:10.1016/j.healthpol.2020.04.008 · Cited by 73 · see citing works
The Opportunities and Risks of Artificial Intelligence in Medicine and Healthcare
Hamid, S. (2016) · Apollo (University of Cambridge) · doi:10.17863/cam.25624 · Cited by 33 · see citing works
Douarin, Y.-M. L., Traversino, Y., Graciet, A., Josseran, A., Bili, A.-B., Blaise, L., Châtellier, G., Coulonjou, H., Delval, C., Detournay, B., et al. (2019) · Therapies · doi:10.1016/j.therap.2019.12.009 · Cited by 25 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Ardan M, Ferry Fadzlul Rahman GBG. The influence of physical distance to student anxiety on Covid-19, Indonesia. J Crit Rev. 2020;7:1126-32, http://dx.doi.org/10.31838/jcr.07.17.141. · DOI
- Koushik CSN, Choubey SB, Choubey A. Chapter 7 -Application of virtual reality systems to psychology and cognitive neuroscience research. In: Sinha GR, Suri JS, editors. Computer modelling, and cognitive science JSBT-CI. Academic Press; 2020. p. 133-47.
Cited by
Sorted by citations
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
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 324 · see citing works
Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12111914 · Cited by 159 · see citing works
Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education
Buabbas, A. J., Miskin, B., Alnaqi, A. A., Ayed, A. K., Shehab, A. A., Syed-Abdul, S., & Uddin, M. (2023) · Healthcare · doi:10.3390/healthcare11091298 · Cited by 152 · see citing works
Tung, A. Y. Z., & Dong, L. W. (2023) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205231201164 · Cited by 84 · see citing works
Ergin, E., Karaarslan, D., Şahan, S., & Bingöl, Ü. (2023) · Journal of Robotic Surgery · doi:10.1007/s11701-023-01592-0 · Cited by 55 · see citing works
Impact of Artificial Intelligence–Based Technology on Nurse Management: A Systematic Review
Gonzalez-Garcia, A., Pérez-González, S., Benavides, C., Pinto-Carral, A., Quiroga-Sánchez, E., & Marqués-Sánchez, P. (2024) · Journal of Nursing Management · doi:10.1155/2024/3537964 · Cited by 53 · see citing works
Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare
Nilsen, P., Sundemo, D., Heintz, F., Neher, M., Nygren, J., Svedberg, P., & Petersson, L. (2024) · Frontiers in Health Services · doi:10.3389/frhs.2024.1368030 · Cited by 39 · see citing works
- Authors
- Sunarti, S., Fadzlul Rahman, F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R.
- Affiliations
- Universitas Muhammadiyah Kalimantan Timur (ID)
- Venue
- Gaceta Sanitaria
- Publisher
- Elsevier BV
- Published
- 2021
- Volume
- 35
- Pages
- S67-S70
- ISSN
- 0213-9111 (ISSN-L)
- OpenAlex
- W3145891727
- Cited by
- 274
- References
- 15
- Citations by year
- 2026: 30 · 2025: 68 · 2024: 70 · 2023: 72 · 2022: 31 · 2021: 3
- FWCI
- 12.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Gaceta Sanitaria (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, AI implementation challenges, clinical decision-making, patient privacy, patient autonomy, ethical issues of AI, cost efficiency, health equity
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Forecasting, Humans, Morals
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