Evidence map›Paper›PMID 40334652›Full record

ArticleJMIR human factors2025

Building and Beta-Testing Be Well Buddy Chatbot, a Secure, Credible and Trustworthy AI Chatbot That Will Not Misinform, Hallucinate or Stigmatize Substance Use Disorder: Development and Usability Study.

Adam Jerome Salyers, Sheana Bull, Joshva Silvasstar, Kevin Howell, Tara Wright, Farnoush Banaei-Kashani

Abstract read
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Adam Jerome SalyersClinic Chat, LLC, 2950 Arkins Ct, Unit 605, Denver, CO, 80216, United States, 1 3038079800.ORCID 0009-0005-1112-5356
Sheana BullClinic Chat, LLC, 2950 Arkins Ct, Unit 605, Denver, CO, 80216, United States, 1 3038079800.ORCID 0009-0004-7110-3266
Joshva SilvasstarClinic Chat, LLC, 2950 Arkins Ct, Unit 605, Denver, CO, 80216, United States, 1 3038079800.ORCID 0000-0002-0816-3555
Kevin HowellUniversity of Texas Health Sciences at San Antonio, San Antonio, TX, United States.ORCID 0009-0009-5073-548X
Tara WrightUniversity of Texas Health Sciences at San Antonio, San Antonio, TX, United States.ORCID 0000-0002-3873-7070
Farnoush Banaei-KashaniDepartment of Computer Science and Engineering, University of Colorado, Denver, CO, United States.ORCID 0000-0003-4102-9873

Funding

Substance Use Disorder Artificially Intelligent chatbot for screening, assessment & referral: SUD BotR41DA059275 · NIDA · CLINIC CHAT LLC · PI BULL, SHEANA S · 2023 to 2023
$301k
NIDA NIH HHS R41 DA059275
6 · The paper itself

Abstract

Background: Artificially intelligent (AI) chatbots that deploy natural language processing and machine learning are becoming more common in health care to facilitate patient education and outreach; however, generative chatbots such as ChatGPT face challenges, as they can misinform and hallucinate. Health care systems are increasingly interested in using these tools for patient education, access to care, and self-management, but need reassurances that AI systems can be secure and credible. Objective: This study aimed to build a secure system that people can use to send SMS with questions about substance use, and which can be used to screen for substance use disorder (SUD). The system will rely on data transfer via third party vendors and will thus require reliable and trustworthy encryption of protected health information . Methods: We describe the process and specifications for building an AI chatbot that users can access to gain information on and screen for SUD from Be Well Texas, a clinical provider affiliated with the University of Texas Health Sciences Center at San Antonio. Results: The AI chatbot system uses natural language processing and machine learning to classify expert-curated content related to SUD. It illustrates how we can comply with best practices in HIPPA (Health Insurance Portability and Accountability Act) compliance in data encryption for data transfer and data at rest, while still offering a state-of-the-art system that uses dynamic, user-driven conversation to dialogue about SUD, screen for SUD and access SUD treatment services. Conclusions: Recent calls for attention to user-friendly design concerning user rights that honor digital rights and regulations for digital substance use offerings suggest that this study is timely and appropriate while still advancing the field of AI.

Indexed as

Artificial IntelligenceComputer SecuritySubstance-Related DisordersText MessagingGenerative Artificial IntelligenceHumansMachine LearningTexasAIartificial intelligencechatbotconversational agentdigital healthHealthcare Insurance Portability and Accountability Acthealth communicationHIPAAinfrastructuresubstance use disorder

Identifiers

PMID40334652
PMCPMC12077853

What OpenQuestion holds

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Registered trials

None linked

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.