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- Deep technologies and safer gambling: A systematic reviewPublication . Cardoso, Leonor G.; Barroso, Beatriz C. R.; Piccoli, Gloria; Peixoto, Miguel; Morgado, Pedro; Marques, António; Rocha, Carla; Griffiths, Mark D.; Queirós, Ricardo; Dores, Artemisa; Pereira da Silva Marques, António José; de Faria Távora Moreira Peixoto, MiguelDeep technologies combine engineering innovation and scientific findings to solve complex problems and are becoming particularly relevant to the gambling industry. With the global rise of gambling practices and the subsequent increase of gambling-related problems and disorders, deep technologies have emerged as a way to create safer online gambling environments. However, there is still limited knowledge regarding their applica bility and consequences. The present study systematically reviewed the existing literature on deep technologies in gambling environments, such as online casinos and betting platforms, and explored their potential benefits, risks, and effectiveness in promoting safer gambling experiences. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Searches were conducted in Web of Sci ence, PubMed, Scopus, EBSCO, and IEEE databases, and manually. A total of sixty-eight studies were included in the review. In general, four primary applications of deep technologies in online settings were found: (i) behavioural monitoring and feedback; (ii) predictive risk modelling; (iii) decision support and AI classifiers; and (iv) limit-setting/self-exclusion tools. They were primarily used to identify and classify problematic gambling, prompt individual action, regulate gambling behaviours, raise awareness of risk levels, promote responsible gambling practices, support research, interventions, and evaluate player protection initiatives. Together, the findings suggest that deep technologies offer ample opportunities to enhance gambler safety and reduce potential risks, although challenges may arise from their implementation, such as privacy and ethical concerns, malicious data use, misclassification of risk levels, and difficulties in large-scale application. Limitations and directions for future studies are discussed