AI for Mental Health

AI-powered tools can analyze speech (e.g., changes in tone, pitch), text (e.g., words that indicate certain emotions, the sentiment of a text), and physiological data (e.g., heart rate variability, galvanic skin response) to detect mental health and neurological conditions. This data can come from EHR, questionnaires, voice recordings, data from wearables, and even information sourced from a patient’s social media.

As a result, AI-powered chatbots can be built to fulfill multiple purposes in the diagnostic process. They could be useful tools for triage. For example, Limbic Access is a mental health chatbot designed to refer patients to a relevant clinician based on a preliminary assessment. Apart from triage, AI assistants can be used to integrate all the data into a comprehensive profile of a patient’s emotional well-being for mental health specialists and even suggest some preliminary diagnosis recommendations for them to review.

Al-powered conversational agents use Natural Language Processing (NLP) to understand input from patients. Then, they can simulate human interactions through text-based or voice communication. In terms of source of the output, AI chatbots can be rule-based (relying on pre-written scripts) or generative (creating original answers using large language models). For instance, a rule-based chat Woebot relies on Cognitive Behavioral Therapy (CBT), Interpersonal Psychotherapy (IPT), and Dialectical Behavioral Therapy (DBT) principles, while Earkick utilizes large language models to provide emotional support based on user input.

AI models can be used to assess patient progress and the effectiveness of therapy over time. For example, Ieso Clinic’s team developed a deep learning model to automatically categorize mental health patients’ statements into CT (change talk) or CCT (counter-change talk) and conducted statistical analysis to determine whether CT statements predict improvement for patients. According to Motivational Interviewing Skill Code (MISC), a coding system for measuring patient adherence in Motivational Interviewing that is used in CBT, CT are statements that express commitment to change, while CTT represent statements that argue against change.

AI can help to evaluate the quality of therapists’ work. One real-life example is an algorithm that analyzes utterances between therapists and clients to reveal how much time is spent on constructive therapy versus general chit-chat during a session. It evaluates text messages and transcribed audio/video calls for the use of evidence-based practices such as motivational interviewing and cognitive behavioral therapy. The system’s feedback can also be used for staff training.

Integrating AI with wearables and specialized mobile apps allows it to track physical activity, sleep patterns, heart rate variability, and even facial expressions. AI algorithms process this data to identify behavioral patterns and specific emotional triggers, which allows them to suggest tailored coping strategies.

AI can help optimize workflows and clinical documentation in mental health organizations. Therapists can use AI-driven therapist assistants to transcribe therapy sessions into structured progress notes, compile treatment plan drafts, and automate the medical coding process. The AI assistants can also summarize clinical notes, identify key facts or inaccuracies in the records, and provide suggestions for therapists before, during, and after client sessions (e.g., suggesting post-session assignments). Such assistants can feature capabilities that help therapists retrieve patient data from a mental health EHR system more efficiently, such as voice commands, smart entry search suggestions, and text-to-speech (for reading patient data out loud to a therapist). Additionally, when integrated with telemedicine solutions, the assistants can help schedule appointments, manage cancellations, fill waitlist slots, and remind patients about upcoming visits.

Machine learning can be used to personalize user experience in non-clinical settings. For example, mindfulness and stress management apps are designed to facilitate self-help and self-monitoring in day-to-day life. Such applications can leverage AI to analyze users’ engagement patterns—such as the time of day they meditate, the duration of their sessions, or their preferred meditation styles (e.g., guided breathing exercises or body scans). This information is then used to tailor the recommended content to the user’s preferences.