Artificial intelligence innovations in substance use prevention on social media: A scoping review.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

On 18 February, 2020, The Lancet published the report of the WHO-UNICEF-Lancet Commission, calling for a refocus of the Sustainable Development Goals (SDGs) around child and adolescent health. All sectors are responsible for children's wellbeing, with digital platforms and artificial intelligence (AI) playing an increasing role in child and adolescent health.

In their editorial, ‘We need better measures to understand the influence of social media on substance use’, Riordan et al. [1] called for greater use of objective indicators of social media use (e.g. smartphone usage logs) and exposure to substance-related content [e.g. passive data collection via browser plug-ins, artificial intelligence (AI)-enabled apps that automatically capture frequency and type of exposures]. We strongly agree, and, in this letter, aimed to highlight the equally important need for research that builds causal evidence concerning social media's influence on substance use. Recent systematic reviews and meta-analyses consistently find statistically significant associations between substance use-related social media exposure and substance use-related attitudes and behaviors among adolescents and young adults [2-5]. Yet, these reviews also emphasize a persistent limitation: many studies to date are cross-sectional, leaving a critical gap in establishing temporality and causation. Without stronger causal data, alcohol, tobacco and other corporate interests—including social media companies—can continue to cast doubt on the idea that exposure to substance use-related content causes harmful outcomes among youth. These industries have repeatedly exploited gaps in causal evidence to delay regulation and accountability [6-10]. There is an urgent need for research designs that strengthen causal inference regarding substance use-related social media exposure and its effects on youth and young adults' substance use-related attitudes and behaviors (e.g. alcohol initiation, escalation and binge drinking). Randomized controlled trials are feasible in this context, as are other approaches, including longitudinal cohort studies with repeated objective measures of social media use, exposures and outcomes over time; and quasi-experimental designs that leverage policy or platform changes to demonstrate population-level effects. For example, provisions of the European Union Digital Services Act restrict targeted advertising to European minors [11]. It also requires social media platforms to give European users the option to choose whether to view posts with or without algorithmic recommendations based on personal data, and to filter out of certain types of content. As a recent parallel, the e-cigarette industry was meaningfully reined in after a compelling evidence base through rigorous experimental studies demonstrated both immediate and long-term harms of youth-targeted marketing (e.g. product placement in music videos; cartoon branding) [12, 13]. This evidence underpinned successful litigation against e-cigarette companies and spurred regulatory reforms (e.g. restrictions on vaping product placement in music videos) [14]. Parallel approaches are essential to build the evidence base at the intersection of social media and all forms of substance use in adolescents and young adults, thereby informing prevention strategies, supporting policymakers and equipping litigators to protect young people from the harms of digital spaces. These efforts are especially urgent, as social media companies face increasing scrutiny for exposing young audiences to harmful content and as awareness grows of the serious health risks associated with substance use. Alex M. Russell: Conceptualization; writing—original draft. Jon-Patrick Allem: Conceptualization; writing—review and editing. During manuscript preparation, generative artificial intelligence tools (GPT-5, Open AI) were used to enhance readability and assist with formatting references. However, these tools did not generate original ideas, contribute substantive text or perform any data analysis. The authors take full responsibility for the content of the manuscript. J.P.A. has received fees for consulting services in court cases pertaining to content on social media platforms. The other authors declare no conflicts of interest.

BackgroundSubstance use–related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use–related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems.ObjectiveThe objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction.MethodsFirst, we provided a narrative summary of the literature on drug use–related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of “infodemiology and infoveillance” in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use–related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology.ResultsWe developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use–related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use–related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied.ConclusionsWe offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Mass media have changed dramatically over the past 25 years, yet they still remain an important channel for substance use prevention messages. Unfortunately, the large mass media substance use prevention campaigns, especially the National Youth Anti-drug Media Campaign, have not been found to be generally effective. Inadequacy of current theories of behavior change, creation of reactance and norms of psychoactive substance use, and failure to target youth at the right age have been offered as explanations. Exposure to messaging is an important issue for campaigns. High exposure to substance use prevention campaigns was often achieved and associated with effectiveness in some studies. Online and social media have added new media platforms for substance use campaigns. Evaluations of web-based interventions show some promise for substance use prevention, although the effects appear modest. Less is known about the effectiveness of social media in substance use campaigns, especially the influence of user-generated content. Many challenges to deploying social media in substance use prevention exist deserving further research, including theory development, measures of effects, selection of appropriate social media formats, and user engagement. Social media also can promote substance use through user-generated content and commercial advertising. Furthermore, monitoring social media can provide insights into new substance use trends that should be addressed in future mass media campaigns.

Rising rates of adolescent overdose deaths attributed to counterfeit prescription drugs purchased using social media have drawn national attention to how these platforms might influence substance use. Research suggests a significant relationship exists between exposure to substance-related social media content and use of drugs and alcohol, but most studies are cross-sectional and limited by recall bias. This study used an ecological momentary assessment (EMA) protocol to collect longitudinal data on social media use and online drug-related exposures associated with youth substance use. Participants, aged 12-23, receiving mental health treatment from a U.S. community-based hospital, joined a six-week, smartphone-based EMA protocol. Each day, participants completed a modified CRAFFT screen for daily substance use and a survey on substance-related online content exposure, and input data from their smartphone screen time reports. Analyses employed mixed effects logistic regression models to explore relationships between substance-related online exposures, substance and social media use. Data was obtained from 25 youth, predominantly white non-Hispanic/Latinx (56.0%) and female (64.0%). Participants had significantly higher odds of substance use on days when exposed to substance-related digital content posted by peers (OR: 19.6). They were also more likely to report these exposures (OR: 7.7) and use substances (OR: 29.6) on days when Snapchat was one of their most frequently used smartphone applications. Our results support existing concerns about specific social media platforms being potential mediators of youth substance use. Future EMA studies in larger cohorts should explore the role of social media platforms in substance procurement.

Prevention of substance (alcohol, tobacco, illegal/legal drug) use in adolescents is a public health priority. As the scope for school-based health education is constrained in school timetables, interventions integrating academic and health education have gained traction in the UK and elsewhere, though evidence for their effectiveness remains unclear. We sought to synthesize the effectiveness of interventions integrating academic and health education for the prevention of substance use. We searched 19 databases between November and December 2015, among other methods. We included randomized trials of interventions integrating academic and health education targeting school students aged 4-18 and reporting substance use outcomes. We excluded interventions for specific health-related subpopulations (e.g., children with behavioural difficulties). Data were extracted independently in duplicate. Outcomes were synthesized by school key stage (KS) using multilevel meta-analyses, for substance use, overall and by type. We identified 7 trials reporting substance use. Interventions reduced substance use generally in years 7-9 (KS3) based on 5 evaluations (d=-0.09, 95% CI [-0.17, -0.01], I2 =35%), as well as in years 10-11 (KS4) based on 3 evaluations (-0.06, [-0.09, -0.02]; I2 =0%). Interventions were broadly effective for reducing specific alcohol, tobacco, and drug use in both KS groups. Evidence quality was highly variable. Findings for years 3-6 and 12-13 could not be meta-analysed, and we could not assess publication bias. Interventions appear to have a small but significant effect reducing substance use. Specific methods of integrating academic and health education remain poorly understood.

Immigrant youth face heightened risks of substance use due to the stress associated with immigration and acculturation. While parental intervention can have a preventative impact on substance use, parents need to be well-informed about substance use and effective interventions that can prevent substance use among immigrant youth. Such interventions ought to be culturally sensitive, family-based, and targeted at the specific substances that are prevalent in a given context. Identifying and curating interventions that can empower parents in addressing substance use can help mitigate the risks that immigrant youth may face. This scoping review aimed to identify the types, characteristics, and effectiveness of family-based substance use intervention programs. Based on Arksay and O'Malley's guidelines, interventions included in the review must have met the following criteria: (a) was a family-based intervention aiming to prevent substance use; (b) targeted immigrant teens aged 12 to 17 years old; (c) was published in English; (d) originated from Australia, Canada, New Zealand, or the United States. The pinch table was used to synthesize included articles, after which studies were compared and categorized, and cross-cutting categories were identified. After screening 4551 searched literature, 13 studies that utilized family-based interventions were included in the review. All interventions were face-to-face programs, and most interventions involved parents and youth as participants. Eco-developmental theory and active learning strategies were used by multiple interventions. Given immigrant families were target stakeholders, both deep structure and surface structure cultural adaptations were utilized. Interventions increased parents' knowledge and skills regarding substance use prevention and delayed substance use initiation among youth. From the review, it was evident that parents are an essential element in any program aiming to prevent or reduce children's substance use. Besides information about substance use prevention, the curriculum also involves parenting and communication skills for parents to understand the protective effects of family. Effective family-based interventions for immigrant youth require attention to parenting and immigration stress, while also considering cultural adaptation. Future directions and limitations are also discussed.

BackgroundProblematic substance use in adolescence and emerging adulthood is a significant public health concern in the United States due to high recurrence of use rates and unmet treatment needs coupled with increased use. Consequently, there is a need for both improved service utilization and availability of recovery supports. Given the ubiquitous use of the internet and social media via smartphones, a viable option is to design digital treatments and recovery support services to include internet and social media platforms.ObjectiveAlthough digital treatments delivered through social media and the internet are a possibility, it is unclear how interventions using these tools should be tailored for groups with problematic substance use. There is limited research comparing consumer trends of use of social media platforms, use of platform features, and vulnerability of exposure to drug cues online. The goal of this study was to compare digital platforms used among adolescents (Generation Zs, age 13-17) and emerging adults (Millennials, age 18-35) attending outpatient substance use treatment and to examine receptiveness toward these platforms in order to support substance use treatment and recovery.MethodsGeneration Zs and Millennials enrolled in outpatient substance use treatment (n=164) completed a survey examining social media use, digital intervention acceptability, frequency of substance exposure, and substance use experiences. Generation Zs (n=53) completed the survey in July 2018. Millennials (n=111) completed the survey in May 2016.ResultsGeneration Zs had an average age of 15.66 (SD 1.18) years and primarily identified as male (50.9%). Millennials had an average age of 27.66 (SD 5.12) years and also primarily identified as male (75.7%). Most participants owned a social media account (Millennials: 82.0%, Generation Zs: 94.3%) and used it daily (Millennials: 67.6%, Generation Zs: 79.2%); however, Generation Zs were more likely to use Instagram and Snapchat, whereas Millennials were more likely to use Facebook. Further, Generation Zs were more likely to use the features within social media platforms (eg, instant messaging: Millennials: 55.0%, Generation Zs: 79.2%; watching videos: Millennials: 56.8%, Generation Zs: 81.1%). Many participants observed drug cues on social media (Millennials: 67.5%, Generation Zs: 71.7%). However, fewer observed recovery information on social media (Millennials: 30.6%, Generation Zs: 34.0%). Participants felt that social media (Millennials: 55.0%, Generation Zs: 49.1%), a mobile phone app (Millennials: 36.9%, Generation Zs: 45.3%), texting (Millennials: 28.8%, Generation Zs: 45.3%), or a website (Millennials: 39.6%, Generation Zs: 32.1%) would be useful in delivering recovery support.ConclusionsGiven the high rates of exposure to drug cues on social media, disseminating recovery support within a social media platform may be the ideal just-in-time intervention needed to decrease the rates of recurrent drug use. However, our results suggest that cross-platform solutions capable of transcending generational preferences are necessary and one-size-fits-all digital interventions should be avoided.

Although the prevalence of artificial intelligence (AI) innovations is on the rise, firms frequently report failures and setbacks in their development and implementation of AI innovation efforts. One common issue behind many failing AI initiatives is that they are organized just like other information technology (IT) innovation efforts. To elucidate why and how the production of AI and IT innovations may need to be managed differently, this study juxtaposes these two types of innovations based on two key dimensions of the Schumpeterian framework: the form (product vs. process) and magnitude (radical vs. incremental) of innovations. By analyzing a matched sample of AI and IT patents, we found robust evidence that AI innovations are less radical and more process oriented than comparable IT innovations. Drawing upon our empirical discovery, we developed a conceptual framework to suggest a new way to think about organizing AI innovation. Our research contributes to the literature and practice on AI innovation by illuminating the comparative differences between AI innovations and other IT innovations and advancing a set of empirically derived propositions on how firms may be able to better manage their AI innovation activities.

BackgroundThe use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application.ObjectiveThis study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation.MethodsOur research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics.ResultsOur findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively.ConclusionsDespite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research.

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Abstract— Artificial Intelligence (AI) models are widely deployed in decision-making systems, but they often exhibit bias due to skewed training data or inherent algorithmic issues. This paper presents a Bias Checker AI Web Application designed to analyze and detect biases in AI-generated outputs. The system uses natural language processing (NLP) and statistical analysis techniques to assess potential biases in text-based predictions. The web-based interface enables [1] real-time bias evaluation, ensuring transparency and fairness in AI systems. The proposed system provides a user-friendly platform for developers and stakeholders to assess their models and mitigate discriminatory outcomes. Additionally, this paper explores the ethical implications of biased AI, potential mitigation techniques, and the importance of transparency in AI-driven decision-making processes. The issue of AI bias extends beyond technical flaws, influencing societal and economic structures by reinforcing stereotypes and discriminatory practices. Addressing bias in AI models is crucial for ensuring fairness in automated decision- making. As AI continues to permeate sectors like finance, healthcare, and law enforcement, biased models can perpetuate historical injustices, leading [14] to tangible negative consequences for marginalized groups. This paper emphasizes the role of bias detection tools in fostering trust and accountability in AI applications. Furthermore, we discuss the significance of incorporating explainability in AI-driven bias detection. The Bias Checker AI Web Application aims to bridge the gap between technical bias analysis and user interpretability, ensuring that results are accessible to both developers and non-technical stakeholders. By integrating intuitive visualization tools and user feedback mechanisms, our system enhances the accessibility of bias detection methodologies. Keywords: Bias detection, AI fairness, Natural Language Processing, Machine Learning, Web Application, Ethical AI, Algorithmic Transparency, AI Ethics.

Abstrak: Di era digital saat ini, kecerdasan buatan (artificial intelligence/AI) memainkan peran yang semakin penting dalam analisis ekonomi; namun, efektivitas berbagai model AI dalam memprediksi indikator ekonomi masih memerlukan evaluasi menyeluruh. Penelitian ini bertujuan untuk mengatasi kesenjangan ini dengan menilai efektivitas model AI dalam konteks yang lebih luas melalui pendekatan tinjauan literatur yang sistematis. Penelitian ini mengidentifikasi metode yang efektif dan mengeksplorasi tantangan dan keberhasilan yang terkait dengan implementasinya. Dengan menggunakan pendekatan penelitian kualitatif dan tinjauan literatur sistematis, literatur yang digunakan bersumber dari database pengindeksan seperti Scopus, DOAJ, dan Google Scholar, dengan tanggal publikasi mulai dari tahun 2014 hingga 2024. Hasil evaluasi menunjukkan bahwa model AI, khususnya deep learning dan model hybrid, menawarkan keuntungan yang substansial dibandingkan metode konvensional dalam memprediksi indikator ekonomi. Jaringan syaraf, seperti LSTM dan CNN, unggul dalam menangkap pola temporal dan spasial yang kompleks, sementara model hibrida meningkatkan akurasi prediksi dengan mengintegrasikan berbagai teknik AI. Penggabungan sumber data alternatif, seperti media sosial dan tren penelusuran, memberikan wawasan tambahan di luar data ekonomi tradisional, sehingga memperkaya prediksi. Explainable AI (XAI) semakin mendukung efektivitas model-model ini dengan meningkatkan transparansi dan kepercayaan di antara para pemangku kepentingan. Selain itu, Natural Language Processing (NLP) meningkatkan akurasi prediksi dengan menganalisis sentimen pasar dan berita ekonomi, sehingga menambah konteks yang berharga.Abstract: In the current digital era, artificial intelligence (AI) plays an increasingly pivotal role in economic analysis; however, the effectiveness of various AI models in predicting economic indicators still requires thorough evaluation. This research aims to address this gap by assessing the effectiveness of AI models within a broader context through a systematic literature review approach. The study identifies effective methods and explores the challenges and successes associated with their implementation. Employing a qualitative research approach and systematic literature review, the literature used is sourced from indexing databases such as Scopus, DOAJ, and Google Scholar, with publication dates ranging from 2014 to 2024. The evaluation results reveal that AI models, particularly deep learning and hybrid models, offer substantial advantages over conventional methods in predicting economic indicators. Neural networks, such as LSTM and CNN, excel at capturing complex temporal and spatial patterns, while hybrid models enhance predictive accuracy by integrating various AI techniques. The incorporation of alternative data sources, such as social media and search trends, provides additional insights beyond traditional economic data, enriching predictions. Explainable AI (XAI) further supports the effectiveness of these models by increasing transparency and trust among stakeholders. Additionally, Natural Language Processing (NLP) enhances predictive accuracy by analyzing market sentiment and economic news, thereby adding valuable context.