AI in Addiction & Mental Health
Understanding How Artificial Intelligence Supports Modern Mental Healthcare
Addiction and mental health conditions are complex, chronic, and often relapsing disorders requiring long-term monitoring and personalized interventions. Artificial Intelligence (AI) and Digital Health technologies are emerging as critical tools to address these gaps, moving care from reactive treatment to proactive support.
Why AI Matters Today
Addressing the rising prevalence, high relapse risks, and limited access to specialist care.
Key AI Technologies
Machine Learning (ML)
Teaching computers to learn from examples instead of programming them with explicit rules. Instead of telling a computer "if temperature >38°C, then fever," we show it thousands of patient records and let it figure out the patterns itself.
In clinical cases: Machine learning involves feeding a computer thousands of clinical notes and allowing it to discover patterns that might be missed, such as which combinations of symptoms predict relapse more effectively than any single factor.
You show the computer 10,000 photos labeled "cat" and 10,000 labeled "not cat." Eventually, it figures out: pointy ears + whiskers + four legs = probably cat. Now it can identify cats in new photos it's never seen.
Instead of coding rules manually, we provide labeled data (like 6,500 clinical notes with annotations) and let algorithms find mathematical patterns. The computer creates a model—essentially a complex equation—that maps inputs (clinical notes) to outputs (substance use categories).
Machine learning algorithms adjust millions of parameters through iterative training to minimize prediction error. In our case, we used supervised learning: the model sees a clinical note, makes a prediction about substance use, gets corrected, and adjusts its internal weights until predictions become accurate.
Large Language Models (LLM)
AI systems trained on massive amounts of text to predict what word comes next, which makes them surprisingly good at understanding and generating human language.
Imagine if someone read the entire internet and became really good at guessing what you'll say next in any conversation. That's essentially what an LLM does—it's seen so much text that it can complete your sentences, answer questions, and even write essays by predicting the most likely next word.
LLMs are AI models trained on billions of words from the internet. They work by predicting what comes next in a sentence—which surprisingly makes them capable of understanding clinical notes, extracting information, and even conversing in ways that feel human-like.
The computer read millions of books, websites, and conversations. Now when you start a sentence, it can guess what comes next because it's seen similar sentences before. String enough predictions together, and you get coherent text.
LLMs are trained on trillions of words to predict the next token (word/part of word) in a sequence. Through this simple task, they learn grammar, facts, reasoning patterns, and even some common sense. GPT-4 has 1.8 trillion parameters—essentially 1.8 trillion knobs that got tuned during training to make better predictions.
LLMs use transformer architecture with self-attention mechanisms to process text. During training (on ~13 trillion tokens for GPT-4), the model learns to represent words as high-dimensional vectors and predict next-word probabilities. What emerges are "emergent abilities"—capabilities like reasoning and instruction-following that weren't explicitly programmed.
Key Capabilities
AI Assistant: Addiction Mx Mentor
An interactive assistant designed to help clinicians access evidence-based information and guidance on addiction management.
To support providers working in addiction and mental health care, we are highlighting an AI-powered clinical mentor tool called Addiction Mx Mentor. This AI assistant is built using advanced language model technology and can respond to queries about:
- Screening and diagnosis
- Evidence-based care strategies
- Substance use and related conditions
- Context-relevant clinical guidance
(Note: You may be asked to sign in to ChatGPT to use it.)
Important Notice
This assistant is a supportive resource and does not replace clinical judgment or formal training. AI outputs should complement standard clinical assessment and evidence-based practice.
Where AI is Used
Decision Support
Clinical decision support systems helping professionals make consistent choices.
Risk Assessment
Predicting relapse risk and checking for signs of deterioration.
Digital Therapeutics
Apps and tools for behavior change and self-monitoring.
Remote Care
Tele-mental health support and outcome monitoring dashboards.
Benefits of AI-Enabled Care
- Earlier identification of risk and deterioration
- More personalized treatment planning
- Better continuity of care between visits
- Improved monitoring of outcomes
- Scalable support in resource-limited settings
Limitations & Responsible Use
AI acts as a support to trained mental health professionals, not a substitute.
- AI outputs depend on the quality of data
- Potential for bias in algorithms
- Predictions are probabilistic, not certainties
- Clinical judgment remains essential
Our Approach at VKN NIMHANS
We explore AI and Digital Health tools with a strict focus on safety and efficacy.