Palavras-chave
Saúde Digital
Inovação em Saúde
Ética Médica
Tecnologia Digital
Como Citar
Resumo
Objetivou-se apresentar o panorama atual da inteligência artificial na saúde, explorando avanços, desafios éticos, técnicos e regulatórios e perspectivas para sua consolidação no cuidado ao paciente. Trata-se de uma revisão integrativa fundamentada em Whittemore e Knafl, com busca nas bases PubMed, SciELO e Google Scholar em janeiro de 2026, incluindo dezoito estudos primários com validação clínica explícita, publicados nos últimos cinco anos nos idiomas português, inglês e espanhol. A seleção e avaliação da qualidade foram conduzidas por dois revisores utilizando a Newcastle-Ottawa Scale, o Critical Appraisal Skills Programme, a ferramenta Cochrane Risk of Bias 2 e o Prediction Model Risk of Bias Assessment Tool. Os dados foram sintetizados por análise temática e síntese narrativa. Os resultados demonstram avanços no diagnóstico por imagem, predição clínica e atenção primária, com acurácia superior ou equivalente à de especialistas humanos. Persistem desafios como viés algorítmico, falta de transparência, vazio regulatório, deterioração pós-implantação e riscos à privacidade e autonomia do paciente. A consolidação ética da inteligência artificial na saúde depende de regulação robusta, diversificação de bases de dados, formação profissional e modelos explicáveis centrados no ser humano.
Referências
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
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