Evidence map›Paper›PMID 42490139›Full record

ReviewJournal of behavioral addictions2026

Deep technologies for responsible gambling: A narrative review of policies and implementation strategies.

Leonor G Cardoso, Beatriz C R Barroso, Eduardo Rocha, Pedro Morgado, António Marques, Carla Rocha, Mark D Griffiths, Ornella Corazza, Ricardo Queirós, Artemisa R Dores

Abstract readReview
In one paragraph

Review in Journal of behavioral addictions, 2026. 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

10 authors.

Leonor G Cardoso1CIR, Escola Superior de Saúde, Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072, Porto, Portugal.ORCID https://orcid.org/0009-0002-8171-7056
Beatriz C R Barroso1CIR, Escola Superior de Saúde, Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072, Porto, Portugal.ORCID https://orcid.org/0009-0002-8050-7532
Eduardo Rocha3Constitutional Law Post-Graduation Program, University of Fortaleza, Fortaleza, Brazil.ORCID https://orcid.org/0000-0003-0972-354X
Pedro Morgado4School of Medicine, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal.ORCID https://orcid.org/0000-0003-3880-3258
António Marques1CIR, Escola Superior de Saúde, Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072, Porto, Portugal.ORCID https://orcid.org/0000-0002-8656-5023
Carla Rocha6Centro de Respostas Integradas do Porto Ocidental, Divisão de Intervenção nos Comportamentos Aditivos e Dependências, Administração Regional de Saúde do Norte, Rua Diogo Botelho, 1651/1653, 4100-268, Porto, Portugal.ORCID https://orcid.org/0000-0002-1588-0501
Mark D Griffiths7International Gaming Research Unit, Psychology Department, Nottingham Trent University, 50 Shakespeare Street, Nottingham NG1 4FQ, United Kingdom.ORCID https://orcid.org/0000-0001-8880-6524
Ornella Corazza8Addiction Science Lab, Department of Psychology and Cognitive Science, University of Trento, via Calepina, 14 - I-38122, Trento, Rovereto, Italy.ORCID https://orcid.org/0000-0001-7371-319X
Ricardo Queirós1CIR, Escola Superior de Saúde, Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072, Porto, Portugal.ORCID https://orcid.org/0000-0002-1985-6285
Artemisa R Dores1CIR, Escola Superior de Saúde, Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072, Porto, Portugal.ORCID https://orcid.org/0000-0003-4595-9816

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Deep technologies, such as artificial intelligence, blockchain, and behavioural analytics, have emerged as promising tools to address complex social challenges. However, their integration into leisure activities, particularly online gambling, has raised concerns over the enhancement of user engagement, which might promote problem gambling behaviours. This review aimed to map how deep technologies intersect with responsible gambling regulations across jurisdictions. Methods: A qualitative scoping review was conducted to synthesise evidence from academic literature, legal and regulatory frameworks, and relevant grey literature. A comparative analysis was performed across key jurisdictions, including the European Union, the United Kingdom, Malta, and selected non-European countries. Results: The review identified very few formal legal instruments that explicitly regulate the use of deep technologies in responsible gambling. The European Union Artificial Intelligence Act introduces binding requirements for high-risk AI systems, including those deployed in gambling, representing a pioneering step toward technology-specific regulation. In contrast, regulatory frameworks beyond Europe were generally non-binding or largely absent, with innovation primarily driven by operators acting voluntarily in the absence of legal mandates. Discussion and conclusions: This analysis underscores persistent gaps in legal oversight and advocates for a shift toward proactive, ethics-based governance models that integrate technological accountability and robust consumer protections. Overall, the findings suggest that regulatory frameworks must not only keep pace with rapid technological innovation but also prioritise prevention through enforceable, transparent, and harmonised policies across jurisdictions.

Indexed as

Artificial IntelligenceBlockchainGamblingEuropean UnionHumansdeep techgamblinggambling addictionpoliciesproblematic gambling

Identifiers

PMID42490139
PMCPMC13678387

What OpenQuestion holds

Textmetadata
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.