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Cited by 5Opioid Use Disorder TreatmentArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?

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Amer, Matthew, Gittins, Rosalind, Millana, Antonio Martinez, Scheibein, Florian, Ferri, Marica, Tofighi, Babak, Sullivan, Frank, Handley, Margaret +3 more·2025·Journal of Medical Internet Research
DOI 10.2196/58723PMC PMC12070021

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Abstract

In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.

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  • Experts have identified eight challenges for equitable deployment in opioid use disorder treatment, including algorithmic transparency, clinical validation, and digital exclusion among low-income individuals.

    “Experts have identified eight challenges for equitable deployment in opioid use disorder treatment, including algorithmic transparency, clinical validation, and digital exclusion among low‐income individuals [12].”

    From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [12]

    The Challenges to Deploying AI Applications section says the authors focus on eight challenges for ethical, effective, and equitable use in OUD treatment. The Implementation Challenges sections identify algorithmic transparency, clinical validation, and digital exclusion, but describe the latter as affecting people who use drugs and other marginalized groups; they do not specifically identify low-income individuals. (LLM, conf=0.86)

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Authors
Amer, M., Gittins, R., Millana, A. M., Scheibein, F., Ferri, M., Tofighi, B., Sullivan, F., Handley, M., Ghosh, M., Baldacchino, A., & Tay Wee Teck, J.
Affiliations
  • University of St Andrews (GB)
  • Aston University (GB)
  • South East Technological University (IE)
  • European Monitoring Centre for Drugs and Drug Addiction (PT)
  • Friends Research Institute (US)
  • University of California, San Francisco (US)
Corresponding author
Amer, Matthew
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2025-04-28
Volume
27
Pages
e58723
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/58723
PMC
PMC12070021
OpenAlex
W4404450662
Cited by
5
References
161
Citations by year
2026: 5
FWCI
2.07
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Discovery Research Portal (University of Dundee) (publishedVersion, cc-by)
  • St Andrews Research Repository (St Andrews Research Repository) (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Opioid Use Disorder Treatment
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
opioid use disorder, big data analytics, artificial intelligence, algorithmic transparency, clinical validation, informed consent, algorithmic bias, digital exclusion, patient trust, AI ethics, health equity
MeSH terms
Big Data, Data Analytics, Artificial Intelligence, Humans, Opioid-Related Disorders

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