Dear Editor, Substance use disorders (SUDs) continue to pose significant challenges in treatment retention and relapse prevention, with global estimates indicating high rates of treatment dropout within the first 90 days.1,2 Addressing these challenges requires innovative approaches that move beyond traditional psychometric assessments and embrace advanced technologies. The integration of artificial intelligence (AI) in clinical practice represents one such promising innovation, particularly in leveraging social media language for predictive analytics. The concept of “digital phenotyping,” which utilizes AI to analyze behavioral and psychological data from online interactions, is gaining traction in mental health research.3 Platforms like Facebook and Twitter provide ecologically valid, real-time datasets reflecting users’ emotional states, coping mechanisms, and interpersonal dynamics.4 By examining linguistic patterns, discussion topics, and sentiment, AI can identify the individuals at risk of poor treatment adherence or relapse, facilitating targeted interventions. Deep learning models such as Bidirectional Encoder Representations from Transformers have demonstrated exceptional capabilities in extracting contextual meaning from text, outperforming traditional models in psychological assessments.5 Recent studies show that transformer-based models can predict treatment dropout with greater accuracy than conventional tools such as the Addiction Severity Index.6,7 These advancements underscore the potential of AI to complement clinical evaluations by addressing the limitations of retrospective self-reporting and interviewer bias. However, implementing such technologies in clinical practice is not without challenges. Ethical considerations surrounding data privacy, informed consent, and potential algorithmic bias must be carefully addressed to ensure equitable and responsible use.8 Furthermore, disparities in digital literacy and social media engagement among diverse populations necessitate inclusive approaches to prevent widening existing healthcare inequities.3 The integration of AI in SUD treatment also opens avenues for personalized care. AI-driven insights may enable clinicians to tailor interventions based on patients’ unique linguistic and behavioral profiles, thereby enhancing engagement and treatment outcomes.6 In addition, real-time monitoring of social media language could function as an early warning system, prompting timely intervention to prevent relapse or disengagement. India, with its rapidly expanding digital ecosystem, is well positioned to harness AI for mental health innovation. Collaborative efforts between technologists, mental health professionals, and policymakers are essential to develop culturally relevant and scalable solutions suited to the Indian context.9 In conclusion, the application of AI to analyze social media language in SUD treatment represents a transformative opportunity to improve the clinical outcomes. Responsible integration of such technologies may help advance personalized, equitable, and effective care for individuals struggling with SUDs. It is hoped that this letter stimulates further dialog and research in this emerging area of mental health innovation. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Neeraj et al. (Wed,) studied this question.