Virtual Psychotherapist

Sun, 01 Jan 2023 00:00:00 +0000

Overview

Access to evidence-based psychotherapy remains severely limited worldwide — constrained by long waitlists, resource scarcity, and geographic disparities. The Virtual Psychotherapist project develops embodied conversational AI agents that complement clinical care by extending access, supporting therapeutic practice, and enabling scalable training.

This initiative is conducted in close collaboration with the University of Lucerne, providing clinical expertise and direct access to real therapy data.

Motivation

Two distinct but complementary challenges motivate this project:

Scaling patient access: Many individuals who would benefit from psychotherapy cannot access it due to cost, waitlists, or geography. AI-based companions that operate between sessions, provide continuous support, and conduct structured therapeutic conversations could meaningfully improve outcomes at scale.
Improving therapist training: Training clinicians in evidence-based interventions requires repeated practice with feedback — but opportunities for safe, standardized training are limited by ethical and resource constraints. Simulated patient systems powered by AI can provide unlimited deliberate practice in controlled settings.

System Architecture

Both applications are built on a shared platform combining:

Large language model-based dialogue: State-of-the-art LLMs for contextually appropriate, therapeutically grounded response generation
Retrieval-augmented generation (RAG): Responses grounded in evidence-based therapeutic literature, reducing hallucinations and ensuring adherence to clinical frameworks
Real-time psychological analysis: Parallel processing pipelines that extract facts, detect psychological flexibility processes, recognize emotions, and monitor safety in real time
Embodied avatar presentation: Synchronized speech synthesis and 3D avatar animation delivered through mobile and desktop applications
Clinician oversight: Structured analysis outputs accessible to supervising therapists, enabling human-in-the-loop clinical governance

Patient-Facing Application

The patient-facing component enables individuals to conduct therapeutic conversations with an embodied avatar between sessions. The system follows process-based therapy principles — particularly Acceptance and Commitment Therapy (ACT), an empirically supported approach targeting psychological flexibility through six core processes: acceptance, cognitive defusion, present-moment awareness, self-as-context, values clarification, and committed action.

Critically, the system is designed for clinical supervision, not autonomous intervention. All session data is structured and accessible to the supervising therapist, who can monitor progress and intervene as needed.

An evaluation against responses from professional psychotherapists demonstrated that the system’s responses were rated significantly higher on understanding, interpersonal effectiveness, collaboration, and ACT alignment — while emphasizing that clinical judgment and the therapeutic relationship remain irreplaceable.

Therapist Training Application

The training application enables psychotherapists to practice and refine therapeutic techniques through role-play interactions with a simulated patient. Key features include:

Clinically grounded patient simulation: Virtual patient behavior conditioned on profiles derived from real therapy sessions, covering a range of clinical presentations and scenarios (suicidality, resistance, heightened anxiety, therapeutic rupture, and more)
Real-time ACT fidelity feedback: An automated evaluator assesses each therapist utterance for adherence to ACT principles, providing immediate visual feedback and the option to retry alternative responses
Configurable scenarios: Therapists can select specific clinical scenarios to target their practice

A systematic evaluation across 49 therapy transcripts identified GPT-4o-mini as the optimal feedback model, achieving the closest alignment with human supervisor ACT fidelity ratings.

Safety and Ethics

Safety is a primary design constraint. The system includes:

Crisis detection: Explicit classification of suicidal ideation and self-harm signals, triggering immediate presentation of crisis resources and mandatory clinician review
Unsafe-interaction detection: Identification of conditions (e.g., active psychosis, mania) where LLM interaction may be counterproductive, with protocol-defined fallback responses
Non-autonomous design: The system is explicitly positioned as a complement to clinical care, not a replacement — structured to require and facilitate clinician oversight

Key Results

Patient-facing system responses rated significantly higher than human therapist responses by automated evaluation and expert psychotherapists across understanding, collaboration, and ACT alignment
Therapist training simulation rated as realistic by practicing psychologists; turn-by-turn feedback shown to increase therapist awareness of intervention choices
Automated ACT fidelity assessment achieves strong agreement with human expert ratings across 49 therapy transcripts

Research Partners

University of Lucerne

RehaBot

Mon, 01 Jan 2024 00:00:00 +0000

Overview

RehaBot is an embodied conversational agent designed to support patients in rehabilitation and home-care settings. The avatar represents a medical professional — capable of conducting structured patient interactions, administering assessments, and delivering health education — to help bridge the gap between in-clinic care and independent recovery at home.

This project is developed in collaboration with Inselspital Bern (University Hospital) and Bern University of Applied Sciences (BFH).

Motivation

Rehabilitation after medical treatment — whether from stroke, orthopedic surgery, cardiac events, or chronic disease — requires sustained patient engagement over weeks or months. Yet contact with healthcare professionals is necessarily episodic, leaving long gaps during which patients must self-manage. Lack of guidance, motivation, and timely feedback during these intervals is a major driver of poor rehabilitation outcomes and preventable hospital readmissions.

An embodied conversational agent that patients can interact with at home — to receive reminders, answer questions, conduct structured assessments, and provide health education — addresses this gap directly. By combining medical knowledge with empathetic communication and a human-like embodied presence, RehaBot aims to make professional-quality support continuously available between clinical appointments.

Approach

RehaBot integrates several complementary AI capabilities within a unified embodied avatar system:

Medical knowledge integration: Structured clinical knowledge relevant to the patient’s rehabilitation pathway, enabling accurate and safe responses to health questions
Conversational assessment: The ability to administer structured health questionnaires and functional assessments through natural spoken dialogue, adapting pacing and clarification to individual patient needs
Empathetic communication: Affective modeling that allows the agent to detect and respond to emotional signals in patient speech — frustration, discouragement, anxiety — with appropriate supportive responses
Health education: Accessible explanations of rehabilitation exercises, medication adherence, warning signs, and self-management strategies, adapted to the patient’s comprehension level
Patient-professional interface: Structured summaries of patient interactions accessible to supervising clinicians, supporting continuity of care and early detection of clinical deterioration

The system is built on the same core platform as the Digital Einstein project, enabling rapid deployment of new capabilities while maintaining consistent embodied presentation quality.

Key Results

Recent work exploring embodied conversational interfaces for personal health data reflection demonstrates that users who engage with health information through a conversational agent formulate significantly more specific and actionable health plans compared to traditional dashboard-based exploration. Embodied conversation lowers the cognitive burden of interpreting health data and supports a shift from passive data inspection to active health sensemaking.

Research Partners

Inselspital Bern (University Hospital of Bern)
Bern University of Applied Sciences (BFH)

Digital Health | Dr. Rafael Wampfler

Virtual Psychotherapist

Overview

Motivation

System Architecture

Patient-Facing Application

Therapist Training Application

Safety and Ethics

Key Results

Research Partners

RehaBot

Overview

Motivation

Approach

Key Results

Research Partners