aceAI applications across HIV prevention, diagnosis and care
Source article: 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
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…
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G 66The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.In brief
An integrative literature review gathered 25 studies with verifiable DOIs, published between 2015 and August 2026, on AI used for HIV prevention, testing and treatment follow-up. The studies covered risk stratification, PrEP indication, automated reading of rapid tests, and prediction of adherence, disengagement and viral suppression.
The review matters because it ties the technical promise of these tools to the daily work of Brazilian nursing, which it frames as central to responsible adoption. What remains open is how far the tools work in local settings, since the authors stress that governance, local validation and human oversight are needed before wide use.
Main points
- The review searched PubMed/MEDLINE, SciELO, the Virtual Health Library/LILACS and DOI tracking, in Portuguese, English or Spanish.
- Applications examined include machine-learning risk stratification, PrEP indication, rapid-test interpretation and counseling support.
- Models were also studied for predicting adherence, disengagement from care and viral suppression.
- Algorithmic equity was a specific topic among the included studies.
- The authors conclude nursing can combine clinical judgment, health education, data protection and person-centered care.
The gain
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 problem
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 rundown
The authors selected twenty-five studies and organized them around prevention, diagnosis and ongoing care for people living with HIV. Prevention work centered on identifying who should be offered pre-exposure prophylaxis, while diagnostic work centered on automated interpretation of rapid tests.
In treatment follow-up, the reviewed studies looked at forecasting which patients might stop engaging with care or fail to reach viral suppression, allowing interventions tailored to individuals. Counseling support tools were also covered.
The paper places these findings in a Brazilian nursing context, arguing that nurses can guide implementation by pairing clinical judgment with health education and data protection while keeping care centered on the person.
What this doesn’t fix
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.
Sources
- Peer-reviewedRevista ft2026-10-15
The debate