Can AI Predict Relapse Before It Happens?

Addiction is a complex, chronic disorder that can affect individuals for long periods of time. Relapse rates are high with studies showing that 40-60% of individuals relapse within the first year following treatment [2]. Not only does addiction take a toll on the individual and their close friends and family, addiction is an economic burden in regards to healthcare system costs and workforce productivity. The opioid crisis alone has cost the U.S. around $1 trillion between 2001 and 2017 and costs employers an estimated $18 billion annually [1]. One of the greatest challenges in addiction treatment is relapse, which can occur even after periods of progress and stability. The identification of warning signs to allow for early intervention is crucial for effective treatment. Current treatment is limited in that it causes delayed intervention, relies on subjective self-reporting, and is embedded with recall bias [1,2]. When considering how to better treat addiction, finding ways to improve the accuracy of relapse prediction is extremely important. Relapse prediction is the process of recognizing warning signs that a person in recovery may be at risk of relapse and intervening before it happens. 

AI has become a common tool in healthcare, such as with cancer diagnosis, but its potential in early detection of relapse is quite promising. One study shows how a hybrid AI and machine-learning model achieved 99.75% accuracy in identifying risk factors associated with diagnosing Internet addiction [1]. AI can use common patterns in behavior, physiology, and patient history to estimate relapse risk [3]. Through collection of real-time data using phones and wearables (such as smart watches), information on mood, sleep quality, mobility, environment, stress, and other physiological metrics can be continuously monitored [2,4]. Additionally, AI models can detect less obvious warning signs like changes in daily routines, decreased social interaction, and irregular sleep [3]. Making use of these metrics, AI can be used to create individualized risk profiles, detect windows of relapse risk, and send alerts for intervention [5].

With AI’s potential for big data analysis and pattern recognition, real-time predictions can be relayed to clinicians and recovery support systems, allowing them to act proactively [3,4]. This opens up the possibility for anticipatory care models and tailored interventions, such as peer support systems and clinician outreach. Preventing relapse before it occurs allows for reinforcement of healthy coping strategies and behavioral cues that might help establish these supportive routines long-term, decreasing future relapse risk. 

AI models can also deliver personalized interactions that offer psychoeducation, coping strategies, and continuous support especially in times of emergency [1]. The advanced natural language processing capabilities of AI models may be able to address accessibility barriers and biases in traditional mental health care, though concerns persist regarding the accuracy of AI in simulating human-like interactions and the therapeutic relationship [1].

Although AI can be a very effective tool to detect potential relapses, AI alone will not comprehensively tackle addiction. Additionally, with the introduction of AI-based monitoring, there are various ethical and privacy concerns to address. To move forward with using this technology in relapse intervention, we must consider the data privacy risks and concerns about surveillance and potential data misuse [3]. Also, AI models must include data from a wide variety of sociocultural environments to prevent underrepresentation of certain populations [3]. 

AI has the potential to transform addiction care by shifting treatment from reactive to proactive. By utilizing real-time data and pattern recognition, periods of elevated relapse risk can be predicted and timely support can be administered. While there is still a long way to the practical implementation of AI in addiction relapse treatment, this technology is promising to improve long-term recovery outcomes from addiction.

Designed by: Jimin Lee

Reviewed by: Wendy House

References: 

[1] Khakpaki, A., & Sepehri, H. (2025, July 24). Ai in addiction: Harnessing technology for diagnosis, prevention, and recovery: A narrative review. Addiction and Substance Abuse. https://www.probiologists.com/article/ai-in-addiction-harnessing-technology-for-diagnosi s-prevention-and-recovery-a-narrative-review 

[2] Mirian Akujuobi, O., Chuks Azu, J., Uzoigwe, Z., & Nelyn Akunna, O. (2025). Digital Therapeutics and AI-Assisted Monitoring for Relapse Prevention in Substance Use Disorders. Healthcare Studies, 3(1), 21–29. https://doi.org/ 10.58612/hs314 

[3] Suva, M., & Bhatia, G. (2024, August 31). Artificial Intelligence in addiction: Challenges and opportunities. Indian journal of psychological medicine. 

https://pmc.ncbi.nlm.nih.gov/articles/PMC11572328/ 

[4] Barndollar, H. (2025, November 4). Ai could predict when someone is going to relapse on opioids. Governing. 

https://www.governing.com/artificial-intelligence/ai-could-predict-when-someone-is-goin g-to-relapse-on-opioids 

[5] Malhotra, D. K. (2022, March 20). The role of Artificial Intelligence (AI) in assisting applied natya therapy for relapse prevention in de-addiction. SpringerLink. 

https://link.springer.com/chapter/10.1007/978-3-030-98404-5_28