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AI in HIV Healthcare:
What It Can and Can't Do

Last reviewed: September 2026

Educational information only — not medical advice. Talk to your healthcare provider about your specific situation.

The CDC, NIH, and HIV.gov are all using artificial intelligence. This site was built with AI. The tools are real, the potential is significant, and the risks require honest discussion. Here's what you actually need to know.

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Artificial intelligence is not coming to HIV healthcare. It is already here — being used by the CDC to track epidemic patterns,[1] by researchers to predict who needs PrEP,[2] by clinics to improve adherence, and by organizations like this one to reach people who wouldn't otherwise find the information they need. Understanding what AI is actually doing in HIV healthcare — and what its real risks and limits are — is now a basic health literacy skill.

This page starts with transparency about this site, because that's the right place to start. Then it covers what AI is doing across the HIV field, what the risks are, and how to engage with AI-generated health information thoughtfully and safely.

🤖 Disclosure: This site — and this article — were built with AI

RiseUpToHIV was built by Kevin Maloney — a person living with HIV since March 2010 and an HIV advocate since 2012 — with the help of multiple AI tools. Kevin has wanted to build a real website like this since 2013, when he launched the No Shame About Being HIV+ Facebook page. For over a decade he had the ideas, the lived experience, and the editorial vision, but not the resources or technical know-how to build a full site on his own. Site construction began in March 2026, once AI tools had matured to the point where a single subject-matter expert could work at the scale of a small team.

The AI stack, and the role each tool plays:

The human owns everything. Every topic on this site, every editorial decision, every structural choice, and every claim the site makes belongs to Kevin. Think of it the way you would think of an electrician who has wired homes for thirty years using modern tools to do the work faster, or a research scientist who owns the discovery even when sophisticated lab equipment does the heavy lifting. The expertise is in the sixteen years of lived, volunteer, and professional HIV experience. AI is the equipment — it lets one advocate finally build the resource he has been carrying around in his head since 2013.

This specific article was drafted with Perplexity and Claude, then cross-checked with ChatGPT and Manus, with ChatGPT also used to help develop images. This is openly disclosed on our Disclaimer page. Kevin reads, reviews, and approves every page before it goes live. The AI tools produce drafts and cross-checks; a human who has lived with HIV for sixteen years decides what stays, what changes, and what the site stands behind.

We believe this is one of the most important examples of what responsible AI use in HIV education looks like: a person with deep lived experience and community credibility, using AI tools as amplifiers of human judgment — not replacements for it — and using more than one model so that no single tool's errors go unchecked. RiseUpToHIV is not an AI chatbot. It is an HIV advocate who used AI to build something bigger than any individual could build alone.

On errors. Just as AI can make mistakes, humans can too — and I am one human who has worked tirelessly for seven months on a site with more than 245 pages. I will not catch every phrasing, every stray word, every small error. If you ever see something on this site that seems wrong, please contact us. A human will read your message and respond, and the site gets better every time the community pushes back on it.

We tell you this because you deserve to know, and because transparency about AI use in health information is one of the ethical requirements we hold ourselves to.

What's happening: AI across the HIV field

The adoption of AI in HIV healthcare has accelerated dramatically since 2022 — when large language models became powerful enough to produce human-quality text, and when the broader healthcare field began taking AI applications seriously. The CDC, NIH, HRSA, and HIV.gov are all actively exploring and deploying AI tools across their functions.[3] Academic medical centers are running AI-powered clinical trials. Community organizations are using AI to create multilingual educational materials. And a growing number of HIV-positive people are already turning to AI chatbots like ChatGPT to get health information — whether or not that use is sanctioned or guided.

The question is no longer whether AI will be part of HIV healthcare. It already is. The question is whether it will be deployed thoughtfully, transparently, equitably, and with meaningful human oversight — or carelessly, in ways that perpetuate existing disparities and introduce new harms.

The Lancet Global Health Commission on AI and HIV — announced in April 2025 — represents the field's recognition that AI in HIV requires dedicated ethical and scientific guidance.[4] The Commission is developing frameworks specifically for equitable AI deployment in HIV settings, with emphasis on communities most affected by both the epidemic and AI bias: Black and Latino communities, transgender populations, people in low-resource settings.

Guidance is being written in real time — and community voices belong in that room

As of late 2026, no single organization has issued comprehensive, formal guidance on AI use in HIV programs. What exists instead is a fast-moving conversation across research institutions, funders, multilateral agencies, community-based advocates, and independent implementers — with early frameworks emerging in parallel and steadily converging. RiseUpToHIV has been part of that conversation from the community-organization side, developing and refining internal practices for AI-assisted HIV education over the last seven months of daily work. What follows are the milestones we're watching and learning from.

AVAC and Audere — November 2025. Released ahead of World AIDS Day 2025, AI and HIV Programs: A Guide for Advocates is a two-page co-branded brief that lays out a four-part opportunity framework — Information, Insights, Innovation, and Trust — and a "Pilot → Pathway to Scale → Scalable Solution" model for community-led AI adoption.[10] The brief names the risks directly: "Weak data privacy protections, biased algorithms, and lack of regulation can deepen inequities and erode trust. To use AI responsibly, every stage of design and deployment must be guided by equity, ethics, and community engagement." It is an early advocate-facing document, not a comprehensive governance framework — AVAC's fuller AI-in-HIV workstream is still being built, and civil society voices are being invited in.

Georgetown / O'Neill Institute working group — October 2025. An 18-member international, interdisciplinary working group — supported by the Bill & Melinda Gates Foundation — published multi-part Recommendations on the Ethical Use of Novel HIV Data & Analytics.[11] The guidance is aimed at researchers, program innovators, and funders, and identifies concrete standards for the responsible use of novel data and machine-learning models in HIV research and programmatic innovation. Alongside the AVAC/Audere brief, this is currently one of the most detailed AI-in-HIV ethics documents in circulation.

UNAIDS Global AIDS Strategy 2026–2031. The next multilateral HIV strategy explicitly names AI as a strategic priority, calling on countries to "leverage AI and digital health" and to "integrate AI and digital health into the global HIV response through clear ethical and technical guidance, support for national implementation and strong community engagement."[12] The strategy also emphasizes using AI to reduce disparities and strengthen data governance, ownership, and privacy protection.

AIDS 2026 in Rio — the field showed up on this. The 26th International AIDS Conference (July 26–31, 2026, Rio de Janeiro) included multiple oral abstracts on AI and machine learning in HIV: risk-factor identification for vertical transmission in Nigeria, ART-adherence prediction in Latin America, AI-assisted TB screening across HIV strata in Brazil, an AI-integrated self-testing device deployed across 17 Chinese provinces, and AI-chatbot telehealth platforms serving LGBTQIA+ populations in India and youth in the Philippines.[13] AVAC convened a session at the conference titled "The digital care continuum: Connecting AI, PrEP, and people." The signal from Rio was clear: AI is no longer a hypothetical in HIV programming — it is being deployed, studied, and evaluated at scale.

One Rio abstract deserves special attention. "Used Right, It Can Save Lives": Community Voices on AI-Driven HIV Risk Prediction in Atlanta, GA interviewed 20 LGBTQ+ community members about AI/machine-learning models that use clinical and STI surveillance data to predict HIV risk.[14] Participants saw real potential — smarter resource allocation, tailored outreach — but insisted on privacy protections, de-identification, transparency, meaningful community engagement, and clear links to actionable care. Their concerns: data misuse, stigma, targeting of vulnerable groups, training-data quality, and representation bias. More than half reported moderate or high concern about using STI surveillance data for AI at all. That is the voice of the community the field is building for.

Where RiseUpToHIV fits in. This site has been built over the past seven months by one person living with HIV, using AI as the writing and research engine, in the absence of formal community-facing guidance from any single authority. In that time we've had to work out — in practice, page by page — what responsible AI-assisted HIV education actually looks like: how to disclose the AI stack, when to defer to a clinician, how to verify every clinical or epidemiological claim against a primary source, when a community publication belongs as narrative voice rather than as citation, how to handle language that risks stigma, and how to handle our own errors when we make them. The principles we hold ourselves to are on this page, in the sections below. As AVAC, Audere, the Georgetown working group, the Lancet Commission, and UNAIDS continue building formal guidance, our hope is that RiseUpToHIV can contribute a community-organization perspective grounded in lived experience — a small, independent voice among the many that will shape how this field learns to use AI well.

How AI is actually being used in HIV healthcare

Epidemiology & Surveillance
Tracking the epidemic and predicting outbreaks
The CDC uses machine learning models to analyze HIV surveillance data — identifying geographic clusters of new infections, predicting outbreak trajectories, and flagging counties where new HIV diagnoses are accelerating. These tools help direct resources and interventions in real time rather than relying on lagging annual reports. AI surveillance models have been deployed to detect injection drug use-related HIV outbreaks in rural areas before they become full-scale epidemics.
Prevention & PrEP
Identifying who needs PrEP and who isn't getting it
Machine learning models can analyze electronic health record data to identify patients who meet clinical criteria for PrEP but haven't been offered it — allowing clinicians to close the gap between who needs PrEP and who receives it. Studies at Harvard and other institutions found that ML-based PrEP identification tools performed significantly better than standard risk assessment tools, catching more people who went on to be diagnosed with HIV if not started on PrEP. AI-powered chatbots are also being deployed to conduct motivational interviewing about PrEP with patients who can't access in-person counseling — in English and Spanish.
Treatment & Adherence
Predicting who will fall out of care and intervening early
AI models trained on clinical data can identify HIV-positive patients at elevated risk of dropping out of care — based on appointment patterns, lab results, medication refill gaps, and social factors. This allows care teams to proactively reach out before someone falls fully out of the system, rather than trying to re-engage them after months of absence. Predictive adherence tools are being piloted at Ryan White clinics across the country. Early results show meaningful improvements in retention in care for high-risk patients when AI-guided outreach is added to standard care.
Drug Discovery & Cure Research
Accelerating the search for a cure
AI is being used to screen vast molecular databases for potential HIV treatment compounds — a task that would take human researchers years to accomplish through traditional methods. Machine learning models are being trained to predict drug resistance patterns based on HIV genotype sequences, helping clinicians choose regimens that are less likely to fail. In cure research, AI is being used to identify biomarkers that predict which patients might achieve long-term remission during analytical treatment interruptions — helping researchers design better trials and identify the people most likely to benefit from experimental cure approaches.
Patient Education & Information
Making HIV information accessible at scale
This is the use case most relevant to people living with HIV directly. AI is being used to create multilingual educational materials, power chatbots that answer HIV questions without judgment, and — as with this site — build comprehensive patient-facing resources that would be impossible for individual advocates to create without AI assistance. Research shows that people living with HIV often prefer asking sensitive questions to a chatbot rather than a clinician, because they fear judgment less. When that chatbot is accurate, culturally competent, and properly limited in scope, it can reach people who otherwise wouldn't seek information at all.
Data to Care
Finding people who have been diagnosed but lost to care
CDC's Data to Care strategy uses surveillance data to identify people who have been diagnosed with HIV but are not currently engaged in care — then works with health departments and community organizations to re-link them. AI tools are being integrated into this process to prioritize outreach — identifying who is most likely to be reached by specific methods (text message, peer navigator, clinic outreach) based on their history. This is one of the most promising applications of AI for closing the gap in the HIV care cascade.

The real risks: what can go wrong

Hallucinations
AI language models generate confident-sounding text that is sometimes factually wrong. In healthcare, a hallucinated drug dosage, interaction warning, or treatment recommendation can cause direct harm. See the hallucinations section below for detail.
Racial and demographic bias
AI models trained on historical healthcare data inherit the biases embedded in that data. Studies have found AI chatbots providing different — and sometimes worse — medical recommendations to Black patients than to white patients. For a disease that disproportionately affects Black communities, this risk is not hypothetical.
Privacy and data security
Sharing HIV status, symptoms, or medication information with AI chatbots raises real privacy questions. Most consumer AI tools use conversation data for training — which means sensitive disclosures may not stay private. HIV status is among the most sensitive categories of health information.
Outdated information
AI models have knowledge cutoffs — they stop learning at a certain date. HIV treatment guidelines change. New drugs are approved. Funding situations shift. An AI trained on data from 2023 may give you outdated information about a topic that has changed significantly since then.
No physical examination
AI cannot examine you. It cannot see symptoms, listen to your lungs, or feel your lymph nodes. It also doesn't know your full medical history, your other medications, or the specifics of your situation. Generalized AI advice cannot substitute for individualized clinical assessment.
Misinformation amplification
AI can produce HIV misinformation — including HIV stigma, outdated information about transmission risk, or inaccurate statements about treatment — with the same confident, authoritative tone it uses for accurate information. There is no visual or tonal difference between AI hallucinations and AI facts.
The appearance of expertise
AI responses sound like they were written by a knowledgeable expert. Studies have found that people rate AI-generated health information as equally or more credible than human-written information. That credibility is not earned — it is a feature of how the technology writes, not an indicator of accuracy.
Lack of accountability
When an AI gives harmful health advice, there is no malpractice. There is no license to revoke. There is no governing board. For human healthcare providers, there are mechanisms of accountability. For AI, those mechanisms are still being developed.

Hallucinations: the specific risk for HIV information

A hallucination, in AI terms, is when a language model generates text that sounds factual and confident but is wrong. The term comes from the way the output resembles a real memory or perception but is actually fabricated by the system's prediction of what text should come next, rather than retrieval of verified facts.

For general topics, hallucinations are annoying. For medical topics — and especially for HIV — they can be dangerous. Examples of the kind of hallucinations that can occur in HIV contexts include: incorrect drug interaction warnings (or missing real ones), wrong information about PrEP dosing schedules, outdated information about HIV criminalization laws, inaccurate statements about transmission risk that could lead to unsafe decisions, and fabricated statistics that sound plausible but are wrong.

The risk is amplified by the fact that AI outputs in health contexts are often mostly correct — which makes the errors harder to detect. A response that gets 19 things right and one thing dangerously wrong feels authoritative. The user has no way to know which of the 20 statements to verify.

A concrete example of HIV-related AI risk: Studies have found that AI chatbots asked about HIV transmission sometimes provide incorrect risk estimates, omit U=U information, or give outdated information about prevention that doesn't reflect current science.[6] Some chatbots have provided information that implicitly stigmatizes PLHIV by overstating transmission risk in ways inconsistent with current evidence. These are not edge cases — they have been documented by researchers specifically studying AI's handling of HIV information.

Equity and bias: the HIV-specific concern

The equity dimensions of AI in HIV healthcare deserve specific attention — because the populations most affected by HIV are the same populations most at risk of being harmed by AI bias.

Training data bias. AI systems learn from the data they're trained on. Medical AI trained primarily on data from majority-white, English-speaking, urban, insured populations will perform worse for Black patients, Latino patients, people who speak languages other than English, rural patients, and uninsured patients. This is not a theoretical concern — multiple studies have documented that AI clinical tools perform less accurately for patients of color,[7] and that some AI chatbots provide different recommendations based on the apparent race or socioeconomic status of the person asking.

HIV stigma in training data. AI models trained on large swaths of internet text absorb the stigma present in that text. Studies have found that AI tools occasionally generate stigmatizing content about HIV and HIV-positive people[8] — reflecting and amplifying the stigma that pervades popular discourse about HIV. An AI that produces subtly stigmatizing responses about HIV is less harmful than an AI that produces overtly wrong medical information, but it is still harmful — particularly when the people it reaches are newly diagnosed and most vulnerable to internalizing stigma.

The access gap in AI benefits. AI-powered HIV tools — advanced diagnostics, PrEP identification algorithms, predictive care engagement tools — are being deployed primarily in well-resourced academic medical centers and health systems with the technical infrastructure to implement them. The rural Southern clinics and community health workers serving the communities with the highest HIV burden are often not the early adopters of these tools. If the benefits of AI in HIV accrue primarily to already well-resourced settings, AI will widen rather than narrow the disparities it has the potential to address.

"AI has the potential to transform HIV interventions; nevertheless, humans need to monitor and ensure that biases are minimized, confidentiality and ethical standards are met, and the risks of AI hallucinations are mitigated. It is essential to integrate AI deliberately, thoughtfully, and with community consultation."
— Kamitani E et al., "The transformative potential of artificial intelligence in the CDC HIV response," AIDS, 2025[5]

What ethical AI use in HIV looks like

For organizations using AI in HIV education and advocacy — including this one — there are principles that distinguish ethical use from careless or harmful use. These are the ones we hold ourselves to.

1
Disclose AI use transparently
People consuming health information deserve to know when AI was involved in creating it. Transparency about AI use is not optional — it is the baseline of trust. This site discloses AI use on its Disclaimer page and here. We believe every health organization using AI should do the same.
2
Human expertise and lived experience must drive the work
AI should amplify human expertise, not replace it. On this site, every page is reviewed and approved by a person who has lived with HIV for 16 years — who catches errors, applies context, and applies judgment that no AI can replicate. HIV education built by AI without meaningful lived experience or clinical oversight is dangerous regardless of how good the writing sounds.
3
Never replace clinical care
AI-generated health information — however accurate and well-sourced — cannot replace individualized clinical assessment. Any AI tool used in HIV contexts should be explicit about this limit, should direct people to appropriate care when clinical questions arise, and should never represent itself as equivalent to medical advice.
4
Verify against trusted sources
All content on this site that involves clinical facts, statistics, or recommendations is verified against CDC, NIH, HRSA, peer-reviewed research, or other authoritative sources. AI-generated text is a starting point, not an endpoint. The work of verification — checking that what the AI produced is actually true — is what makes the difference between helpful health information and dangerous misinformation.
5
Protect privacy — never use sensitive disclosures for AI training
HIV status, symptoms, treatment history, and related personal information should never be shared with AI tools that use conversation data for training. When someone reaches out to this site, their information goes to a human — not to an AI system that will learn from it.
6
Center equity — who isn't being reached?
AI tools in HIV should be evaluated not just by how well they work for typical users, but by how well they serve the communities most affected by the epidemic: Black and Latino communities, rural Southerners, transgender people, people who inject drugs, undocumented immigrants. If an AI tool serves English-speaking insured urban patients well but fails everyone else, it is not a solution to the HIV epidemic — it is a tool that benefits the already advantaged.

For patients: how to use AI health information safely

If you're already using ChatGPT, Claude, Gemini, or other AI tools to look up HIV information — and many people are — here is how to do it more safely.

Use AI for general education, not clinical decisions. AI is good at explaining what a CD4 count is, what U=U means, how PrEP works, or what the difference is between integrase inhibitors and protease inhibitors. It is not a substitute for your HIV provider making a specific recommendation about your specific situation.

Verify anything clinical before acting on it. If an AI tells you something about your medications, your labs, your treatment options, or your legal rights — verify it against a trusted source (CDC, NIH, your provider, this site) before making any decision based on it. Treat AI medical information the way you'd treat medical information from a well-read friend: worth considering, worth verifying, not worth acting on uncritically.

Don't share your HIV status with general AI tools. Major consumer AI platforms (ChatGPT, Gemini, some others) use conversation data to train their models. Sharing your HIV diagnosis, your medications, your test results, or other sensitive health information creates privacy risks. If you need to discuss your specific situation, do it with a healthcare provider — not an AI that may retain and learn from your disclosure.

Ask AI to cite sources. When using an AI tool for health information, ask it where the information comes from. A well-designed AI should be able to point you toward CDC, NIH, or peer-reviewed research for factual claims. If it can't — or if it fabricates citations — that's a signal to verify more carefully.

Notice when the confidence level matches the certainty level. AI tools typically write with the same confident, authoritative tone regardless of whether they're highly certain of a fact or guessing. For medical information, pay attention to whether the AI acknowledges uncertainty — a tool that never says "I'm not sure" or "this varies by individual situation" may be overconfident.

The test: Would you act on this information if a trusted HIV provider told you the same thing, or would you want to verify first? Apply the same standard to AI output. If you'd verify it from a human, verify it from an AI too — and the verification process is easier, not harder, when AI tells you where to look.

The future: where AI and HIV are heading

The trajectory of AI in HIV healthcare over the next five to ten years will be shaped by how successfully the field navigates the tensions between AI's genuine potential and its real risks.

The potential is significant. Long-acting PrEP delivered by AI-supported outreach systems could dramatically expand prevention access in communities where clinic-based PrEP delivery has failed. AI-powered surveillance that identifies outbreak clusters in real time could prevent the kind of slow-building rural HIV epidemics that caught the public health system off guard in Indiana and West Virginia. AI clinical decision support that ensures every HIV-positive patient in a Ryan White clinic gets evidence-based care regardless of their provider's experience level could meaningfully reduce disparities in care quality.

The risks are real but manageable with appropriate oversight, transparency, and equity focus. The HIV community — which has a 40-year history of demanding meaningful participation in the research and policy that affects it — is well-positioned to ensure that AI in HIV healthcare is developed with community input rather than imposed on communities without it. The Denver Principles[9] — the 1983 declaration by people with AIDS that the epidemic's most affected communities must have a central role in shaping the response — apply as much to AI deployment as to drug development.

What is certain is that AI will be part of HIV healthcare for the foreseeable future. The question is not whether to engage with it but how — with eyes open to both its potential and its limits, with human oversight that doesn't erode in the name of efficiency, and with an uncompromising commitment to centering the communities most affected by the epidemic in every application of the technology.

This site is one model of what that can look like. One HIV-positive advocate. One AI tool. An 84-page website built in response to a state-level funding crisis, reaching people who wouldn't otherwise have found this information. That's not AI replacing HIV advocacy — that's HIV advocacy using AI to do what advocates have always done: reach people with information they need, at scale, with no corporate funding and no institutional gatekeepers.

References & Sources

  1. CDC, "Examples of CDC Programs Currently Using AI" — public overview of AI use in CDC surveillance, prevention, and outreach, including HIV. ↩
  2. Krakower DS et al., "Development and validation of an automated HIV prediction algorithm to identify candidates for pre-exposure prophylaxis" (Atrius Health, ~1.15M patients; cv-AUC 0.86–0.91); companion publication in Lancet HIV, 2019. ↩
  3. HIV.gov, "How HHS Agencies Are Using Artificial Intelligence to Support the HIV Response" — overview of AI initiatives across CDC, NIH, HRSA, HIV.gov. ↩
  4. Wong ELY, Chan D, Tucker JD et al., "The Lancet Global Health Commission on AI and HIV: leveraging AI for equitable and sustainable impact," Lancet Global Health, April 2025. See also PubMed 40155096. ↩
  5. Kamitani E, Cavallo C, Pals S et al., "The transformative potential of artificial intelligence in the CDC HIV response," AIDS, July 2025 — CDC-authored review of AI applications and community-engagement gaps in HIV surveillance and prevention. ↩
  6. Ayers JW et al., "Comparing Physician and AI Chatbot Responses to Patient Questions," JAMA Intern Med, 2023 — accuracy limits of general-purpose medical chatbots. See also HIV-specific chatbot evaluations in Kamitani 2025. ↩
  7. Obermeyer Z et al., "Dissecting racial bias in an algorithm used to manage the health of populations," Science, 2019 — foundational documentation of racial bias in a widely deployed U.S. clinical algorithm. ↩
  8. Nadarzynski T et al., "Barriers and facilitators to engagement with artificial intelligence-based chatbots for sexual and reproductive health advice," and related evaluations of stigma in generative AI outputs. ↩
  9. People With AIDS Advisory Committee, "The Denver Principles" (1983) — founding statement of the HIV patient-empowerment movement: "Nothing about us without us." ↩
  10. AVAC & Audere, "AI and HIV Programs: A Guide for Advocates" (November 2025) — co-branded two-page brief released ahead of World AIDS Day 2025, framing responsible AI use in HIV around Information, Insights, Innovation, and Trust. Landing page at PrEPWatch. ↩
  11. O'Neill Institute for National and Global Health Law (Georgetown University), "Recommendations on the Ethical Use of Novel HIV Data & Analytics" (October 2025) — multi-part guidance from an 18-member international, interdisciplinary working group supported by the Bill & Melinda Gates Foundation. ↩
  12. UNAIDS, "The Global AIDS Strategy 2026–2031" — explicitly names AI and digital health as a strategic priority requiring clear ethical and technical guidance, national implementation support, and strong community engagement. ↩
  13. International AIDS Society, Abstracts from AIDS 2026, the 26th International AIDS Conference (Rio de Janeiro & virtual, 26–31 July 2026) — including OAE0302, OAE0303, OAE0305, OAD3606LB, OAE0306LB, OAE3104, OAD3905, and OAE1604 on machine-learning and AI-chatbot interventions in HIV. AVAC's AIDS 2026 Conference Roadmap lists the session "The digital care continuum: Connecting AI, PrEP, and people" (30 July 2026). ↩
  14. Patino-Mateus JD, Quiroga-Gutierrez N, Ramon JS, Moscovich T, Ordóñez CE, Mena L, Saldana CS — "'Used Right, It Can Save Lives': Community Voices on AI-Driven HIV Risk Prediction in Atlanta, GA" (Abstract OAE0304, AIDS 2026). ↩
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