Evidence map›Paper›PMID 40294410›Full record

ArticleJournal of medical Internet research2025

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

Matthew Amer, Rosalind Gittins, Antonio Martinez Millana, Florian Scheibein, Marica Ferri, Babak Tofighi, Frank Sullivan, Margaret Handley, Monty Ghosh, Alexander Baldacchino and 1 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Matthew AmerNHS Tayside, Ninewells Hospital, Dundee, United Kingdom.ORCID 0000-0002-4466-9844
Rosalind GittinsAston Pharmacy School, Pharmaceutical & Clinical Pharmacy Research Group, College of Health and Life Sciences, Aston, United Kingdom.ORCID 0000-0002-0893-7936
Antonio Martinez MillanaUniversitat Politècnica de València, Valencia, Spain.ORCID 0000-0003-1056-5067
Florian ScheibeinSouth East Technological University, Waterford City, Ireland.ORCID 0000-0001-9585-5068
Marica FerriEuropean Monitoring Centre for Drugs and Drug Addiction, Lisbon, Portugal.ORCID 0000-0001-5195-0653
Babak TofighiFriends Research Institute, Baltimore, MD, United States.ORCID 0000-0001-7393-7564
Frank SullivanDigitAS Project, Population and Behavioural Science Research Division, School of Medicine, University of St Andrews, St Andrews, United Kingdom.ORCID 0000-0002-6623-4964
Margaret HandleyDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA, United States.ORCID 0000-0001-5034-2111
Monty GhoshDepartment of Medicine, Cumming School of Medicine, 2500 University Drive NW, Calgary, AB, Canada.ORCID 0000-0002-1566-0015
Alexander BaldacchinoDigitAS Project, Population and Behavioural Science Research Division, School of Medicine, University of St Andrews, St Andrews, United Kingdom.ORCID 0000-0002-5388-7376
Joseph Tay Wee TeckDigitAS Project, Population and Behavioural Science Research Division, School of Medicine, University of St Andrews, St Andrews, United Kingdom.ORCID 0000-0001-5199-7013

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceBig DataOpioid-Related DisordersData AnalyticsHumansAIalgorithmartificial intelligencebig datadeep learningearly detectionearly warningmachine learningMLopioidopioid useopioid use disorderpractical modelpredictive analyticspredictive modelpredictive systemsubstance usesubstance use disorder

Identifiers

PMID40294410
PMCPMC12070021

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.