Evidence map›Paper›PMID 41632955›Full record

ArticleJournal of medical Internet research2026

Evaluation of an Artificial Intelligence Conversational Chatbot to Enhance HIV Preexposure Prophylaxis Uptake: Development and Usability Internal Testing.

Jun Tao, Ellie Pavlick, Amaris Grondin, Josue D Bustamante, Harrison Martin, Hannah Parent, Natalie Fenn, Alexi Almonte, Amanda Maguire-Wilkerson, Mofan Gu and 5 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jun TaoDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0000-0002-1940-8634
Ellie PavlickDepartment of Computer Science, Brown University, Providence, RI, United States.ORCID http://orcid.org/0000-0002-7155-5420
Amaris GrondinDepartment of Computer Science, Brown University, Providence, RI, United States.ORCID http://orcid.org/0009-0008-6378-3408
Josue D BustamanteSchool of Electrical Engineering and Computer Science, College of Engineering, Oregon State University, Corvallis, OR, United States.ORCID http://orcid.org/0009-0008-8508-0359
Harrison MartinDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0000-0002-5222-0908
Hannah ParentDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0009-0001-4054-5676
Natalie FennDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0000-0002-7926-1953
Alexi AlmonteDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0009-0004-7510-9956
Amanda Maguire-WilkersonDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0009-0009-7653-3069
Mofan GuDivision of Infectious Diseases, The Miriam Hospital, Providence, RI, United States.ORCID http://orcid.org/0000-0001-9050-7531
Jack RusleyDepartment of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, United States.ORCID http://orcid.org/0000-0002-1626-2289
Bryce K PerlerDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0000-0003-1362-2522
Tyler WrayDepartment of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, United States.ORCID http://orcid.org/0000-0003-4545-9141
Amy S NunnDepartment of Behavioral and Social Sciences, Brown University School of Public Health, Providence, RI, United States.ORCID http://orcid.org/0000-0001-8115-0390
Philip A ChanDepartment of Medicine, The Warren Alpert Medical School of Brown University, Room 130, 11 4th Street, Providence, RI, 02906, United States, 1 401-863-1000.ORCID http://orcid.org/0000-0003-0964-5895

Funding

An AI-based behavioral intervention to improve pre-exposure prophylaxis for HIV preventionR34MH138136 · NIMH · MIRIAM HOSPITAL · PI CHAN, PHILIP ANDREW, TAO, JUN · 2025 to 2025
$705k
An AI-Based Chatbot for Doxycycline as Post-Exposure Prophylaxis Among Men Who Have Sex with MenR21AI192507 · NIAID · MIRIAM HOSPITAL · PI Philip Andrew Chan, Jun Tao · 2025 to 2026
$427k
NIAID NIH HHS R21 AI192507NIMH NIH HHS R34 MH138136
6 · The paper itself

Abstract

Background: The HIV epidemic in the United States disproportionately impacts gay, bisexual, and other men who have sex with men (MSM). Despite the effectiveness of HIV preexposure prophylaxis (PrEP) in preventing HIV acquisition, uptake among MSM remains suboptimal. Motivational interviewing (MI) has demonstrated efficacy at increasing PrEP uptake among MSM but is resource-intensive, limiting scalability. The use of artificial intelligence, particularly large language models with conversational agents (ie, "chatbots") such as ChatGPT, may offer a scalable approach to delivering MI-based counseling for PrEP and HIV prevention. Objective: This internal usability testing aimed to evaluate the development of an artificial intelligence-based chatbot, including its ability to provide MI-aligned education about PrEP and HIV prevention and potential to support PrEP uptake. Methods: The Chatbot for HIV Prevention and Action (CHIA) was built on a GPT-4o base model embedded with a validated knowledge database on HIV and PrEP in English and Spanish. The CHIA was fine-tuned through training on a large MI dataset and prompt engineering. The use of the AutoGen multiagent framework enabled the CHIA to integrate 2 agents, the PrEP Counselor Agent and the Assistant Agent, which specialized in providing MI-based counseling and handling function calls (eg, assessment of HIV risk), respectively. During internal testing from March 10-April 28, 2025, we systematically evaluated the CHIA's performance in English and Spanish using a set of 5-point Likert scales to measure accuracy, conciseness, up-to-dateness, trustworthiness, and alignment with aspects of the MI spirit (eg, collaboration, autonomy support) and MI-consistent behaviors (eg, affirmation, open-ended questions). Descriptive statistics and mixed linear regression were used to analyze the data. Results: A total of 296 responses, including 145 English responses and 151 Spanish responses, were collected during the internal testing period. Overall, the CHIA demonstrated strong performance across both languages, receiving the highest combined scores in the general response quality metrics including up-to-dateness (mean 4.6, SD 0.8), trustworthiness (mean 4.5, SD 0.9), accuracy (mean 4.4, SD 0.9), and conciseness (mean 4.2, SD 1.1). The CHIA generally received higher combined scores for metrics that assessed alignment with the MI spirit (ie, empathy, evocation, autonomy support, and collaboration) and lower combined scores for MI-consistent behaviors (ie, affirmation, open-ended questions, and reflections). Spanish responses had significantly lower mean scores than English responses across nearly all MI-based metrics. Conclusions: Our internal usability testing highlights the potential of the CHIA as a viable tool for delivering MI-aligned counseling in English and Spanish to promote HIV prevention and support PrEP uptake, though its Spanish language performance requires further improvement.

Indexed as

Artificial IntelligenceHIV InfectionsPre-Exposure ProphylaxisGenerative Artificial IntelligenceHomosexuality, MaleHumansMaleMotivational Interviewingartificial intelligencecounselingHIV infectionsmotivational interviewingmultiagent frameworkpreexposure prophylaxis

Identifiers

PMID41632955
PMCPMC12867473

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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 OpenQuestion graph.