aceInteligência artificial aplicada à prevenção, ao diagnóstico e ao cuidado de pessoas vivendo com HIV: desafios éticos, evidências científicas e perspectivas para a enfermagem brasileira
This article analyzed scientific evidence on the application of Artificial Intelligence (AI) to HIV prevention, diagnosis, and care, emphasizing ethical challenges and perspectives for Brazilian nursing. An integrative literature review was conducted using PubMed/MEDLINE, SciELO, the Virtual Health Library/LILACS, and DOI tracking mechanisms, including publications from 2015 to August 2026 in Portuguese, English, or Spanish. Twenty-five studies with verifiable DOI were selected, covering machine-learning applica…
Across 25 reviewed studies, AI tools could widen identification of priority populations for HIV prevention, aid clinical decisions, and personalize follow-up of people on treatment.
The same AI applications in HIV care carry risks of data bias, opaque models, privacy breaches, stigma and technological dependence that call for local validation and professional oversight.
The source reports that data bias, model opacity, privacy, stigma, information security and technological dependence remain unresolved risks that require governance, local validation and professional oversight.
Evidence
- Peer-reviewedRevista ft2026-10-15
How should this claim be treated?
Truvace Impact Record TRV-2026-1652, v1: “Inteligência artificial aplicada à prevenção, ao diagnóstico e ao cuidado de pessoas vivendo com HIV: desafios éticos, evidências científicas e perspectivas para a enfermagem brasileira.” Truvace, 2026-10-17. /record/TRV-2026-1652 (accessed at citation time). sha256 916bb1756e8da912…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1652 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.