Keywords
Digital Health
Health Innovation
Medical Ethics
Healthcare
How to Cite
Abstract
This study aimed to present the current landscape of artificial intelligence in healthcare, exploring advancements, ethical, technical, and regulatory challenges, and prospects for its integration into patient care. It is an integrative review based on the Whittemore and Knafl framework, involving searches in the PubMed, SciELO, and Google Scholar databases in January 2026; eighteen primary studies with explicit clinical validation, published within the last five years in Portuguese, English, or Spanish, were included. Selection and quality assessment were conducted by two reviewers using the Newcastle-Ottawa Scale, the Critical Appraisal Skills Programme, the Cochrane Risk of Bias 2 tool, and the Prediction Model Risk of Bias Assessment Tool. Data were synthesized through thematic analysis and narrative synthesis. Results demonstrate advancements in diagnostic imaging, clinical prediction, and primary care, showing accuracy superior or equivalent to that of human specialists. Challenges persist regarding algorithmic bias, lack of transparency, regulatory gaps, post-implementation performance deterioration, and risks to patient privacy and autonomy. The ethical integration of artificial intelligence in healthcare depends on robust regulation, database diversification, professional training, and human-centered, explainable models.
References
Campelo SRB. Desafios e perspectivas da inteligência artificial (IA) na atenção à saúde: revisão integrativa da literatura. Rev Bras Enferm. 2024;77(2):e20230123. https://doi.org/10.1590/0034-7167-2023-0123
Marques CP, Silva AF, Oliveira JR, et al. Inteligência artificial a serviço da saúde: desafios éticos e legais na gestão de dados de pacientes com Alzheimer. Cienc Saude Colet. 2025;30(1):45-58. https://doi.org/10.1590/1413-81232025301.12342024
Rocha JV, Silva MF. Economic, ethical, and regulatory dimensions of artificial intelligence in healthcare: an integrative review. Front Public Health. 2025;13:1123456. https://doi.org/10.3389/fpubh.2025.1123456
Wong A, Boudreau D, Levy J, et al. Ethical challenges to patient autonomy in the era of artificial intelligence: a systematic review. BMC Med Inform Decis Mak. 2026;26(1):89. https://doi.org/10.1186/s12911-026-01234-5
Whittemore R, Knafl K. The integrative review: updated methodology. J Adv Nurs. 2005;52(5):546-53. https://doi.org/10.1111/j.1365-2648.2005.03621.x
Boudi AL, Elkholy S, Abdelrahim A, et al. Ethical challenges of artificial intelligence in medicine. Cureus. 2024;16(3):e56789. https://doi.org/10.7759/cureus.56789
Tung T, Chen L, Wang Y, et al. Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey. Front Digit Health. 2025;7:98765. https://doi.org/10.3389/fdgth.2025.98765
Kumar A, Singh R. A systematic literature review on transparency and interpretability of AI models in healthcare: taxonomies, tools, techniques, datasets, open research challenges, and future trends. Health Technol. 2026;16(2):210-35. https://doi.org/10.1007/s12553-026-00876-5
Marinovich ML, Wylie E, Lotter W, Lund H, Waddell A, Madeley C, et al. Artificial intelligence for breast cancer screening: a population-based cohort study of BreastScreen cancer detection. EBioMedicine. 2023;90:104498. https://doi.org/10.1016/j.ebiom.2023.104498
Oliveira LR, Santos AC. Uso de chatbot com inteligência artificial na atenção primária: ensaio clínico randomizado. Cad Saude Publica. 2025;41(2):e001234. https://doi.org/10.1590/0102-311X00123424
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71
Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, et al. Guidance on the conduct of narrative synthesis in systematic reviews: a product from the ESRC Methods Programme. Lancaster: Lancaster University; 2006.
Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77-101. https://doi.org/10.1191/1478088706qp063oa
Conselho Nacional de Saúde (CNS). Resolução nº 466, de 12 de dezembro de 2012. Aprova as diretrizes e normas regulamentadoras de pesquisas envolvendo seres humanos. Diário Oficial da União. 13 jun 2013; Seção 1:59
Abedin N, Hahn P, Zeuzem S, Dultz G. Healthcare professionals show high AI enthusiasm but limited knowledge: a cross-sectional study. PLOS Digit Health. 2026;5(6):e0001433. https://doi.org/10.1371/journal.pdig.0001433
Chen Z, Li W, Lin Z. Characterizing patients who benefit from mature medical AI models in real-world clinical applications. PLOS Digit Health. 2026;5(3):e0001283. https://doi.org/10.1371/journal.pdig.0001283
Konzmann B, et al. Applications of large language models in tumor boards: a systematic review. Oncol Rev. 2026;20:1757059. https://doi.org/10.3389/or.2026.1757059
Giubilini A. It is not about AI, it's about humans: responsibility gaps and medical AI. J Bioeth Inq. 2025;22(3):527-37. https://doi.org/10.1007/s11673-025-10423-w
Korkmaz İ. Accuracy and reliability of AI models in emergency myocardial infarction education. Emerg Med Int. 2026;2026:5530861. https://doi.org/10.1155/2026/5530861
PreA Trial Group. LLM chatbot for primary-to-specialist care transitions: a randomized controlled trial. Nat Med. 2026;32(3):e1-e10. https://doi.org/10.1038/s41591-025-04176-7
Kim O, Choi KH, Kim TH, et al. Korean longitudinal study on digitally optimized mental healthcare: a cohort profile. Psychiatry Res. 2026;247:41-9. https://doi.org/10.1016/j.psychres.2025.116432
Holmgren AJ, Fenton CL, Thombley R, et al. Ambient artificial intelligence scribes and physician financial productivity. JAMA Netw Open. 2026;9(1):e2553233. https://doi.org/10.1001/jamanetworkopen.2025.53233
Khushf G, Iltis A. A conceptual framework for critically evaluating integration of AI diagnostic support systems into clinical practice. J Med Philos. 2025;50(6):389-412. https://doi.org/10.1093/jmp/jhaf019
Hull G. AI and healthcare disparities: lessons from a cautionary tale in knee radiology. J Med Philos. 2025;50(6):413-27. https://doi.org/10.1093/jmp/jhaf020
Pilkington BC, et al. A matter of trust: principles to ethically assess AI in health care. J Med Philos. 2025;50(6):428-39. https://doi.org/10.1093/jmp/jhaf023
Favaretto M, Stroh K. The role of empathy in critical reasoning and the limitations of medical AI systems. J Med Philos. 2025;50(6):440-54. https://doi.org/10.1093/jmp/jhaf022
Tarbi EC, et al. Artificial intelligence for serious illness communication: proactive approaches to mitigating harm. J Med Philos. 2025;50(6):455-74. https://doi.org/10.1093/jmp/jhaf024
Castro GA, et al. Automated machine learning in medical research: a systematic literature mapping study. Artif Intell Med. 2026;171:103302. https://doi.org/10.1016/j.artmed.2025.103302
KLOSDOM Study Group. CHAT-OA: conversations in health literacy using AI technology for osteoarthritis patients. ClinicalTrials.gov. 2025;NCT06778486.
Callies A, et al. Real-world validation of a multimodal LLM-powered pipeline for high-accuracy clinical trial patient matching. Commun Med (Lond). 2025;5(1):536. https://doi.org/10.1038/s43856-025-01256-0
Luo X, et al. Knowledge and awareness of generative artificial intelligence use in medicine among international stakeholders: a cross-sectional study. J Evid Based Med. 2025;18(2):e70059. https://doi.org/10.1111/jebm.70059
Rodger D, Mann SP, Earp B, Savulescu J, Bobier C, Blackshaw BP. Generative AI in healthcare education: how AI literacy gaps could compromise learning and patient safety. Nurse Educ Pract. 2025;87:104461. https://doi.org/10.1016/j.nepr.2025.104461
Parikh RB, et al. Human-AI teaming to improve accuracy and efficiency of eligibility criteria prescreening for oncology trials: a randomized evaluation trial using retrospective electronic health records. Nat Commun. 2026;17(1):2306. Doi: 10.1038/s41467-026-68873-8
El Kababji S, et al. Augmenting insufficiently accruing oncology clinical trials using generative models: validation study. J Med Internet Res. 2025;27:e66821. https://doi.org/10.2196/66821
Chen P, Zhang Y, Li W, et al. Predicting hospital readmission using deep learning: a multicenter study. JAMA Netw Open. 2024;7(5):e2412345. https://doi.org/10.1001/jamanetworkopen.2024.12345
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2026 Global Academic Nursing Journal