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Use of artificial intelligence within the gambling field: a scoping review protocol

[version 1; peer review: awaiting peer review]
Ståle Pallesen
https://orcid.org/0000-0002-5831-0840
1,2, Elise Constance Fodstad1-3, Conchita Sisi Martin1,2,4, Farangis Sharifibastan1,2, Rune Krumsvik5, Hailemariam Weldemariam1,2,6
Ståle Pallesen
https://orcid.org/0000-0002-5831-0840
1,2, Elise Constance Fodstad1-3, [...] Conchita Sisi Martin1,2,4, Farangis Sharifibastan1,2, Rune Krumsvik5, Hailemariam Weldemariam1,2,6
PUBLISHED 20 Aug 2025
Author details Author details
OPEN PEER REVIEW
REVIEWER STATUS AWAITING PEER REVIEW

This article is included in the Artificial Intelligence and Machine Learning gateway.

This article is included in the Gambling and Gaming Addiction collection.

Abstract

Introduction

This scoping review aims to map existing studies that have employed artificial intelligence (AI) tools within the gambling field, examining their areas of use, current trends, and key findings.

Methods and analysis

This review will adhere to the Joanna Briggs Institute Reviewers’ Manual. The review will be organized along the Population, Concept and Context approach. It will include quantitative peer-reviewed studies that examine the use of AI tools within gambling contexts. Searches for relevant articles will be conducted in Web of Science, APA PsycINFO, Medline (Ovid), ProQuest, CINAHL, and Wiley Online Library. A search for grey literature will be conducted in GreyLit. Org, ProQuest Dissertations and Theses, Google Scholar, and Google search engine, reviewing the first 50 results in Incognito mode. Two independent reviewers will perform screening, selection, and data extraction, with disagreements resolved through discussion or consultation with a third reviewer. The results will be presented in graphical and tabular format, accompanied by a narrative summary following the PRISMA-ScR guidelines. The protocol has been pre-registered in Open Science Framework: https://doi.org/10.17605/OSF.IO/FMBE6

Ethics and dissemination

This study protocol is exempted from ethical approval. The planned review aims to describe how AI has been used within the gambling field and has as such as a goal to inform various stakeholders such as clinicians, gambling operators as well as regulatory authorities. The scoping review will be published in an open access journal.

Keywords

artificial intelligence, machine learning, gambling, scoping review, protocol.

Corresponding author: Ståle Pallesen
Competing interests: No competing interests were disclosed.
Grant information: The author(s) declared that no grants were involved in supporting this work.
Copyright:  © 2025 Pallesen S et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Pallesen S, Fodstad EC, Martin CS et al. Use of artificial intelligence within the gambling field: a scoping review protocol [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:807 (https://doi.org/10.12688/f1000research.167592.1) First published: 20 Aug 2025, 14:807 (https://doi.org/10.12688/f1000research.167592.1) Latest published: 20 Aug 2025, 14:807 (https://doi.org/10.12688/f1000research.167592.1)

Introduction

Gambling involves staking money or other valuables on the outcome of a game or event determined, partially or entirely, by chance.1 Today, gambling activities occur in the vast majority of cultures and can be traced back 4,000 years.2 Prevalence studies indicate that almost half of adults worldwide have gambled in the past 12 months.3 Globally, it is estimated that 8.7% of adults engage in risky gambling, while 1.4% struggle with problematic gambling.3 The diagnosis of gambling disorder is reserved for the most severe cases and is characterized by impaired control over gambling, an increasing priority given to gambling, and the continuation or escalation of gambling despite negative consequences.4

The global gambling market was valued at approximately $540 billion in 2023, and is expected to grow 6.4% annually, driven by factors such as legalization, urbanization, increased social media use, and a rising population.5 Various technological advancements are anticipated to spur growth in online gambling channels, including gambling apps and online casinos.5,6 Responsible gambling initiatives aim to promote awareness and prevent harms associated with gambling, but many of these have faced criticism for placing one onus of responsibility on the individuals.7 Wardle et al.8 argue that the growth of the gambling industry is facilitated by strong ties to governments, and emphasize that health related harms of problematic gambling should take precedence over economic interests, commercial profitability, and government revenue generation. Online gambling is expanding rapidly, and in parallel so does the amount of player account data,9 which facilitates the use of various artificial intelligence (AI) technologies.10

AI refers to computer systems that automatically perform complex tasks such as perceiving, reasoning, decision making, problem-solving, and learning, which require intelligence when conducted by humans.11–13 Some AI’s technologies mine data, learn, recognize speech and images, and analyze cognitions and emotions.11 Machine learning involves computer algorithms that identify patterns in data and enhance their performance automatically over time.14,15 Among machine learning subsets, deep learning employes a group of machine learning algorithms to execute high-level abstractions in data, resembling human brain functions, using deep architectures or interconnected nodes that constitute multiple non-linear transformations. Deep learning systems automatically learn features at multiple levels of abstraction, enabling them to learn complex functions and map raw sensory input data to the output without human intervention.16 Deep learning techniques can be applied in areas such as speech recognition,17 object recognition,18 and natural language processing.19 On the other hand, artificial neural networks, which resemble biological neural networks, perform specific tasks similarly to that of the human brain, using hundreds or thousands of interconnected artificial neurons or nodes that process and transmit information.20,21 AI has revolutionized automated online service interactions through advancement in algorithms, massive data and affordable computational power and storage.22 By transforming online consumer behavior into actionable strategies, AI-based digital market businesses can access their customers just-in-time.23–25 Features like speech recognition and machine learning, enhance mixed reality (MR), immersion, enjoyment and consumers’ perception, positively impacting engagement, purchase intentions and social sharing.26 Studies also highlight AI’s ability to strengthen relationships between digital markets and online users.27,28

In the realm of gambling, AI systems collect and process various forms of consumer data in real time to enhance the understanding of customers (Dos Santos, 2015). For example, models based on neural networks have been developed to predict bet amounts and cumulative winnings/losses.29 Several studies have used AI to identify problem gamblers.30–33 In one study, Auer and Griffiths34 used objective account based data from online casino players and matched it with self-reported gambling problems. Based on data such as wagering, depositing, and gambling frequency, random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling. The two AI algorithms achieved an area under the curve (AUC) value of.73 and.67, respectively, which is considerably better than chance.34 Other studies using machine learning models have exclusively used account based data, showing that early behaviors (within the first 7 days) can predict later high risk gambling.35 Machine learning models, exclusively based on survey data have shown that reports of gambling problems can be predicted by various demographic and self-reported gambling-related behaviors.36 Suicidal ideation and suicide attempts among gamblers have also been modelled with machine learning using similar data.37 In addition, AI has also been used to identify personality traits that distinguish between gambling disorder patients and healthy controls.38 One study used mixed logistic regression machine learning based on data from four bet types and eight seasons of English Premier League soccer, finding that losses were positively correlated with observable betting odds. The authors suggest incorporating differences in product risk into consumer education and responsible gambling approaches.39 Another application of AI within the gambling sphere concerns predicting outcomes in skill-based games (e.g., poker). Studies have shown that AI can outperform top poker players in some cases.40,41 Research has also been conducted to assess and validate the skills of poker players against AI-simulated games.42 A qualitative interview study with gambling industry stakeholders suggests that while AI holds promise for consumer protection, there is also an inherent risk of increased player exploitation (e.g., creating more addictive games and more persuasive marketing tactics).43

Based on this backdrop, the aim of this scoping review is to map studies where AI-based tools have been used within the gambling field, areas of use as well as current trends and findings.

Protocol

We decided to conduct a scoping review on the topic of AI use within the gambling field as both fields recently have undergone rapid changes. The use of AI seems to explode within many sectors.44 Parallelly, gambling is moving towards digital and online games.45 This enables AI to have a large impact on the emerging gambling field. In light of the lack of current reviews on the topic in question, we thus found it timely to systematically map the use of AI within the gambling field by employing a scoping review approach. This review will be conducted following JBI’s scoping review methodology46 and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews; PRISMA-ScR) guidelines.47 It was not deemed appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research as the research concerns a literature review.

Review questions

The aim of the review is to provide an overview of how and for which purposes AI is used within the gambling field and describe various AI-models used, trends thereof and the main empirical findings from the identified studies. This lead to the development of the following research questions: 1) How are AI-technology and AI-tools used (purposes) within the gambling field?, and 2) What characterizes the AI-models used, what are the trends in AI technology use, and what are the main findings?

Inclusion and exclusion criteria

The inclusion and exclusion criteria will follow the participants-concept-context model outlined by Peters et al.46

Participants. No specific criteria regarding participants will be applied in the current study.

Concept. The central concept concerns the use of AI-tools within the gambling field. These tools refer to computer systems that automatically accomplish complex tasks, such as reasoning and decision making, which require intelligence when conducted by humans. Specifically, these tools encompass machine learning, deep learning, reinforcement learning, neural networks, natural language processing, supervised and unsupervised learning, decision trees, probabilistic models, Bayesian network, extreme gradient boosting, long short-term memory, generative adversarial networks, CatBoost, and random forests.

Context. Gambling implies staking money on a future outcome that is at least partly determined by chance. Eligible studies will broadly cover the use of AI-tools broadly within the field of gambling, and address issues such as identifying specific groups of gamblers (e.g., problem gamblers), and modelling human behavior and decision making in gambling, often where skills are involved. Studies on gamblers’ attitudes towards AI-incorporated features in gambling will not be eligible as these do not relate to the actual use of AI.

Type of sources

This review we will include only quantitative peer-reviewed studies where AI-tools have been used in the context of gambling. Reviews and qualitative studies will not be included. Studies will be sourced from scientific databases (see later) and journals (backwards tracking). Abstracts, and theses will not be included. Only articles written in English will be considered.

Search strategy

The search strategy will follow a three-step approach as outlined in the JBI Manual for Evidence Synthesis for Scoping Reviews.46

  • 1. Initial search: A targeted search will be conducted in APA PsycINFO, Web of Science, and Google Scholar to identify relevant studies and refine the search terms. The search string will include the concept term “artificial intelligence” and relevant subcategories (“machine learning”, “deep learning”, “reinforcement learning”, “neural networks”, “natural language processing”, “NLP”, “supervised learning”, “unsupervised learning”, “decision trees”, “probabilistic models”, “Bayesian network”, “Extreme Gradient Boosting”, “XGBoost”, “Generative Adversarial Network”, “GANs”, “CatBoost”, and “Random Forest”) combined with the term gambl*. Boolean operators will be used, with “OR” connecting related terms within each group/category and “AND” linking the two main categories (AI and gambling). A preliminary search will cover the period from December 2024 to January 2025 and will be adapted to the specific characteristics of APA PsycINFO, Web of Science, and Google Scholar. The results of this search will be reviewed to identify additional keywords and index terms, which will inform the final search strategy (see Table 1).

  • 2. Refined search strategy: The refined search strategy will be applied to additional databases to ensure comprehensive coverage. These databases will include Medline (Ovid), ProQuest, CINAHL, and Wiley Online Library. Given the relatively recent emergence of this topic, we have decided not to impose any time restrictions on articles for inclusion. The search is restricted to English-language publications, as most relevant studies and AI methodologies are reported in English. Including additional keywords and indexing terms from the initial search will enhance the breadth and accuracy of the refined strategy. Boolean operators will be used again to maximize the retrieval of relevant studies across databases. For exclusion criteria, articles referring to gaming (e.g., video gaming or e-sports) will not be considered, as the focus of this review is specifically on gambling-related contexts.

  • 3. Identifying additional studies: Manual screening of reference lists from included articles and searches of grey literature will be conducted. Grey literature searches will cover sources such as GreyLit. Org, ProQuest Dissertations and Theses, Google, and Google Scholar, with the first 50 results reviewed in Incognito mode to reduce bias from browsing history. The search results, including records retrieved, screened, and included, will be documented and visualized using the PRISMA flow diagram.48 In terms of grey literature, only conference proceedings and dissertations will be considered due to the emerging nature of the topic. Other types of publications will be excluded as they do not meet the necessary quality standards. The adequacy of the selected publications will be assessed using the ACCOODS Checklist49 for grey literature.

Table 1. Draft search strategy.

DatabaseQueryRecords retrieved
Web of Sciencegambl* AND ((artificial intelligence) OR (machine learning) OR (deep learning) OR (reinforcement learning) OR (neural networks) OR (natural language processing) OR (NLP) OR (supervised learning) OR (unsupervised learning) OR (decision trees) OR (probabilistic models) OR (Bayesian network) OR (Extreme Gradient Boosting) OR (XGBOOST) OR (Large Language Model) OR (LLM) OR (Generative Adversarial Network) OR (GANS) OR (CatBoost) OR (Random Forest))388

Selection of papers

The Covidence Systematic Review software50 will be used to manage references throughout the review process. All citations identified through the search process will be uploaded to Covidence for duplicate removal and initial screening by title and abstract. A pilot screening will be performed on 10 of the identified citations by two independent reviewers (CSM and FS) to ensure consistency in applying the eligibility criteria. Following this, a consensus meeting will be held to discuss the pre-defined inclusion and exclusion criteria, with adjustments made if necessary. After the pilot screening, the remaining citations will be screened by title and abstract in line with the eligibility criteria. Screening will be conducted by two independent reviewers (CSM and FS), and studies deemed eligible will be advanced for full-text review. In the full-text review stage, two independent reviewers (CSM and FS), will evaluate all selected studies. Reasons for excluding studies during this phase will be systematically documented and included in the final report. Any disagreements between reviewers during the selection process will be resolved through discussion and consensus with a third reviewer (SP). The search results and study selection process will be detailed and presented in a PRISMA flow diagram,48 ensuring transparency and adherence to scoping review reporting standards.

Data extraction

Data extraction will follow the strategy of “data charting” described in the JBI template.46 We developed a draft charting form that will be adjusted after the pilot extraction (See Table 2). We will extract data on authorship, year and type of publication, country of origin, study objective, methodology, population, AI characteristics, type of gambling, and key findings. The studies will be categorized according to our review question.

Table 2. Draft data charting form.

Publication details
Author(s)
Year of publication
Type of publication
Country of origin
Language
Aims/purpose/objective
Population and sample size
Methodology/design
AI and gambling details
AI characteristics
 -AI model
 -Model complexity
 -Algorithms
 -Type of supervision
 -Task performed
 -Availability of source code
Gambling characteristics (e.g., online/offline, lottery)
Categorizations regarding our review questions1
Key findings

During the pilot extraction, all reviewers will independently extract data from the same 10 papers. A meeting with all reviewers will then serve to calibrate the extraction process and adjust the charting form. Further extraction will be done by two independent reviewers (CSM and FS). Disagreements will be resolved in a discussion meeting with the two reviewers, and a third reviewer (SP) will be involved if agreement is not achieved.

Data analysis, presentation and dissemination

Search strategy and study selection will be reported following the PRISMA flowchart.48 Results will be presented in tabular (and graphical) format, along with a narrative summary. Descriptive statistical reporting frequencies will be presented. The presentation will follow the recommendations by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR).47 A paper will be produced and sought published in an open access journal.

Discussion

AI is used increasingly within the gambling industry. Based on both subjective data as well as player account data AI holds promise as a tool for predicting future gambling behavior, identifying those with problems as well as validating specific skills involved in gambling. Within the gambling context AI may thus be beneficiary for customers, however the potential for misuse in terms of customer exploitation cannot be dismissed. We regard the scoping review approach as the best suited method to gain an overview and to make a synthesis regarding how AI currently is being used within the gambling industry as well as pointing out future and coming trends. The review will inform various stakeholders (e.g., regulatory agencies, the industry, clinicians as well as various user organization) about the use of AI within the gambling field. A limitation of the planned scoping review is that it will be restricted to publications in English. In addition, unpublished paper/report made by the industry will not be included.

Data availability

No data is associated with this article.

References

  • 1.  Bolen DW, Boyd WH: Gambling and the gambler. A review and preliminary findings. Arch. Gen. Psychiatry. 1968 May; 18(5): 617–630. Publisher Full Text
  • 2.  Suissa AJ: A critical perspective on gambling: A sociohistorical analysis. Zangeneh M, Blaszczynski A, Turner NE, editors. In the pursuit of winning: problem gambling theory, research and treatment. New York, NY: Springer; 2008; pp. 119–133. Publisher Full Text
  • 3.  Tran L, Wardle H, Colledge-Frisby S, et al.: The prevalence of gambling and problematic gambling: a systematic review and meta-analysis. Lancet Public Health. 2024 Aug; 9(8): e594–e613. PubMed Abstract | Publisher Full Text
  • 4.  World Health Organization: International classification of diseases for mortality and morbidity statistics. Geneva: World Health Organization; 11th (ICD-11) ed. 2022.
  • 5.  The Business Research Company: Gambling global market report 2024. Hyderabad: The Business Research Company; 2024 [05.01.2025]. Reference Source
  • 6.  Le T: Global casinos & online gambling - market research report (2014-2029). New York, NY: 2024. Reference Source
  • 7.  Reynolds J, Kairouz S, Ilacqua S, et al.: Responsible gambling: a scoping review. Crit. Gambl. Stud. 2020; 1(1): 23–39. Publisher Full Text
  • 8.  Wardle H, Degenhardt L, Marionneau V, et al.: The Lancet Public Health Commission on gambling. Lancet Public Health. 2024 Nov; 9(11): e950–e994. PubMed Abstract | Publisher Full Text
  • 9.  Delfabbro P, Parke J, Catania M: Behavioural tracking and profiling studies involving objective data derived from online operators: A review of the evidence. J. Gambl. Stud. 2024 Jun; 40(2): 639–671. PubMed Abstract | Publisher Full Text | Free Full Text
  • 10.  Davenport TH: The AI advantage: how to put the artifical intelligence revolution to work? Cambridge, MA: Mit Press; 2018.
  • 11.  Abbass H: What is artificial intelligence? IEEE Trans. Artif. Intell. 2021 Apr; 2(2): 94–95. Publisher Full Text
  • 12.  Russell S, Norvig P: Artificial intelligence: a modern approach. Pearson; 4th ed.2021.
  • 13.  Winston PH: Artifical intelligence. Reading, MA: Addison-Wesley Publishing Company; 3rd ed. 1992.
  • 14.  Alzubi J, Nayyar A, Kumar A: Machine learning from theory to algorithms: an overview. J. Phys. Conf. Ser. 2018; 1142: 012012. Publisher Full Text
  • 15.  Domingos P: A few useful things to know about machine learning. Commun. ACM. 2012 Oct; 55(10): 78–87. Publisher Full Text
  • 16.  Deng L: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process. 2014 Jan; 3(e2): 1–29. Publisher Full Text
  • 17.  Hinton G, Deng L, Yu D, et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 2012 Nov; 29(6): 82–97. Publisher Full Text
  • 18.  Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. Commun. ACM. 2017 Jun; 60(6): 84–90. Publisher Full Text
  • 19.  Mikolov T, Yih W-T, Zweig G, editors. Linguistic regularities in continuous space word representations. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Atlanta, Ga: 2013.
  • 20.  Kufel J, Bargiel-Laczek K, Kocot S, et al.: What is machine learning, artificial neural networks and deep learning? Examples of practical applications in medicine. Diagnostics. 2023 Aug; 13(15): article 2582. PubMed Abstract | Publisher Full Text | Free Full Text
  • 21.  López OAM, López AM, Crossa J: Multivariate statistical machine learning methods for genomic prediction. Cham, Switzerland: Springer; 2022.
  • 22.  Perez-Vega R, Kaartemo V, Lages CR, et al.: Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework. J. Bus. Res. 2021 May; 129: 902–910. Publisher Full Text
  • 23.  Duan YQ, Edwards JS, Dwivedi YK: Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda. Int. J. Inf. Manag. 2019 Oct; 48: 63–71. Publisher Full Text
  • 24.  Haenlein M, Kaplan A: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manag. Rev. 2019 Aug; 61(4): 5–14. Publisher Full Text
  • 25.  Ransbothan S, Kiron D, Gerbert P, et al.: Reshaping business with artificial intelligence. MIT Sloan Management Review and The Boston Consulting Group; September 2017. Reference Source
  • 26.  Sung E, Bae S, Han DID, et al.: Consumer engagement via interactive artificial intelligence and mixed reality. Int. J. Inf. Manag. 2021 Oct; 60: 102382. Publisher Full Text
  • 27.  Blomster M, Koivumäki T: Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing. Inf. Syst. E-Bus. Manag. 2022 Mar; 20(1): 123–169. Publisher Full Text
  • 28.  Ullal MS, Hawaldar IT, Soni R, et al.: The role of machine learning in digital marketing. SAGE Open. 2021 Oct; 11(4): 1–12. Publisher Full Text
  • 29.  Chan VKY: Using neural networks to model the behavior and decisions of gamblers, in particular, cyber-gamblers. J. Gambl. Stud. 2010 Mar; 26(1): 35–52. PubMed Abstract | Publisher Full Text
  • 30.  Hopfgartner N, Auer M, Helic D, et al.: Using artificial intelligence algorithms to predict self-reported problem gambling among online casino gamblers from different countries using account-based player data. Int. J. Ment. Heal. Addict. 2024 May. Publisher Full Text
  • 31.  Kairouz S, Costes JM, Murch WS, et al.: Enabling new strategies to prevent problematic online gambling: a machine learning approach for identifying at-risk online gamblers in France. Int. Gambl. Stud. 2023 Sep; 23(3): 471–490. Publisher Full Text
  • 32.  Murch WS, Kairouz S, Dauphinais S, et al.: Using machine learning to retrospectively predict self-reported gambling problems in Quebec. Addiction. 2023 Aug; 118(8): 1569–1578. PubMed Abstract | Publisher Full Text
  • 33.  Murch WS, Kairouz S, French M: Establishing the temporal stability of machine learning models that detect online gambling-related harms. Comput. Hum. Behav. Rep. 2024 May; 14: 100427. Publisher Full Text
  • 34.  Auer M, Griffiths MD: Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting. J. Gambl. Stud. 2023 Sep; 39(3): 1273–1294. PubMed Abstract | Publisher Full Text | Free Full Text
  • 35.  Auer M, Griffiths MD: Gambling before and during the COVID-19 pandemic among online casino gamblers: An empirical study using behavioral tracking data. Int. J. Ment. Heal. Addict. 2023; 21(3): 20–27. PubMed Abstract | Publisher Full Text | Free Full Text
  • 36.  Seo W, Kim N, Lee SK, et al.: Machine learning-based analysis of adolescent gambling factors. J. Behav. Addict. 2020 Sep; 9(3): 734–743. Publisher Full Text
  • 37.  Mohajeri M, Towsyfyan N, Tayim N, et al.: Prediction of suicidal thoughts and suicide attempts in people who gamble based on biological-psychological-social variables: a machine learning study. Psychiatry Q. 2024 Dec; 95(4): 711–730. PubMed Abstract | Publisher Full Text
  • 38.  Cerasa A, Lofaro D, Cavedini P, et al.: Personality biomarkers of pathological gambling: A machine learning study. J. Neurosci. Methods. 2018 Jan; 294: 7–14. PubMed Abstract | Publisher Full Text
  • 39.  Hassanniakalager A, Newall PWS: A machine learning perspective on responsible gambling. Behav. Public Policy. 2022 Apr; 6(2): 237–260. Publisher Full Text
  • 40.  Mittal A, Sahoo S: A case for legalizing poker in the age of artifical intelligence: an international perspective on gambling and betting laws. Gaming Law Rev. 2020 Jun; 24(5): 331–336. Publisher Full Text
  • 41.  Moravcík M, Schmid M, Burch N, et al.: DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science. 2017 May; 356(6337): 508–513. PubMed Abstract | Publisher Full Text
  • 42.  MacKay TL, Bard N, Bowling M, et al.: Do pokers players know how good they are? Accuracy of poker skill estimation in online and offline players. Comput. Human Behav. 2014 Feb; 31: 419–424. Publisher Full Text
  • 43.  Ghaharian K, Binesh F, Soligo M, et al.: AI ethics in a controversial industry: the case of gambling and its ethical paradox. AI Ethics. 2025 July; 5: 1685–1701. Publisher Full Text
  • 44.  Davenport TH, Barkin I: All hand on tech. The AI-powered citizen revolution. Hoboken, NJ: Wiley; 2024.
  • 45.  Liu MTC, Dong SY, Zhu MX: The application of digital technology in gambling industry. Asia Pac. J. Mark. Logist. 2021 Jun; 33(7): 1685–1705. Publisher Full Text
  • 46.  Peters MDJ, Godfrey C, McInerney P, et al.: Scoping reviews.Aromataris E, Lockwood C, Porritt K, et al., editors. JBI manual for evidence synthesis. Joanna Briggs Institute Collaboration; 2020.
  • 47.  Tricco AC, Lillie E, Zarin W, et al.: PRISMA extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med. 2018 Oct; 169(7): 467–473. Publisher Full Text
  • 48.  Page MJ, McKenzie JE, Bossuyt PM, et al.: The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Med. 2021 Mar; 18(3): e1003583. PubMed Abstract | Publisher Full Text | Free Full Text
  • 49.  Tyndall J AACODS Checklist. Flinders University; 2010. [cited 2025 Jan 31]. Reference Source
  • 50.  Macdonald M, Misener M, Weeks L, et al.: Covidence vs Excel for the title and abstract review stage of a systematic review. Int. J. Evid. Based Healthc. 2016; 14(4): 200–201. Publisher Full Text

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Pallesen S, Fodstad EC, Martin CS et al. Use of artificial intelligence within the gambling field: a scoping review protocol [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:807 (https://doi.org/10.12688/f1000research.167592.1)
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