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)
Large Language Models (LLMs) are a class of Artificial Intelligence (AI) systems built to generate, understand and modify human language by learning patterns from large data sets.
What is a Token?
A token is the basic unit of text that AI language models process—think of it as the model's equivalent of a 'word,' though not exactly the same. Tokenizers break text into these discrete elements, which may be whole words, parts of words, or even individual characters.
Example: "pharmacotherapy" might be split as:
- • 1 token: "pharmacotherapy" (whole word)
- • 2 tokens: "pharmaco" + "therapy"
- • 3 tokens: "pharma" + "co" + "therapy"
Or even smaller fragments.
Example: "buprenorphine" might be split as:
- • 1 token: "buprenorphine" (whole word)
- • 2 tokens: "bup" + "renorphine"
- • 3 tokens: "bu" + "pren" + "orphine"
Or even smaller fragments.
This chunking strategy allows models to:
- Handle rare medical terms or technical vocabulary efficiently
- Process text in any language
- Capture meaning across long clinical narratives
- Understand context and relationships between concepts
Tokens also include spaces, punctuation, and special markers like "<EOS>" (end of sequence). Importantly, the model never sees actual words or letters—only these tokenized sequences. This is why token limits matter: a 4,000-token context window might hold roughly 3,000 words of clinical text, but complex medical terminology may consume more tokens per word.
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
How Do LLMs Work?
A Brief Overview for Clinicians
1. The Mechanics: Self-Attention & Training
Self-attention allows the model to weigh different parts of an input and determine which words are most relevant in predicting the next token. LLMs are trained on vast amounts of text from books, websites, articles, and dialogue through unsupervised learning—meaning they learn by predicting missing or next tokens in sequences. Training involves adjusting billions of parameters through an iterative process to minimize predictive error. With sufficient exposure to human language, LLMs develop implicit knowledge of grammar, syntax, semantics, and even abstract reasoning, enabling them to generate contextually relevant outputs across diverse tasks without task-specific training.
2. Evolution & Emergent Abilities (Brief GPT History)
The evolution from GPT-1 (2018) to current models like GPT-4, Claude, and Gemini demonstrates how scale enables capability. Early models required explicit fine-tuning for each task, but later generations developed 'emergent abilities'—complex skills like reasoning, planning, and following natural language commands—that appeared when models exceeded certain size thresholds. This allows modern LLMs to function as general-purpose problem solvers rather than simple text predictors.
3. Safety Alignment & Clinical Relevance
Beyond initial training, LLMs undergo instruction tuning (learning to follow commands) and reinforcement learning from human feedback (RLHF), where human raters evaluate outputs for helpfulness, safety, and appropriate tone. This process helps models become more empathetic, supportive, and clinically appropriate—especially important when discussing sensitive topics like addiction and mental health.
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.
Acknowledgement
Dr Lekhansh Shukla, Dr Aishwariya Jha, Dr Prakrithi Shivaprakash and Heads AI team
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