ReviewThe lancet. HIV2026
Artificial intelligence in HIV research, policy, and clinical care.
Review in The lancet. HIV, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Dating Applications as HIV Prevention Ecosystems in the Era of Digital Sexual Networks.Infectious disease reports · 2026Article
- How AI responds to common HIV/AIDS questions: ChatGPT versus DeepSeek.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
A large and rapidly growing percentage of health-care organisations are implementing artificial intelligence (AI; including generative AI), for research, care delivery, and policy. Despite concerns (eg, ethical considerations and data privacy) by HIV stakeholders (eg, providers, patients, government, and community-based organisations), AI-driven tools are already being used in HIV-related areas such as testing, risk prediction, clinical decision making, and HIV interventions. This Viewpoint outlines the use of AI in both HIV and broader public health settings; provides examples across patient, provider, and system-level use cases; and presents crucial AI-related implementation challenges, including health literacy, system readiness, and equitable access. HIV stakeholders need to actively and repeatedly seek out and engage partnerships with industry and technology professionals to ensure that AI safely and effectively supports public care and trust.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.