Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence

University Hospital Basel

Status: Active, not recruiting

Conditions: Mental Disorder

Treatments: Behavioral: High Levels of Common Therapeutic Factors; Behavioral: Standard motivational interviewing; Behavioral: Low Levels of Common Therapeutic Factors

Study type: Interventional

Funder types: Other

Identifiers

NCT06813066

0000-00000 th24Meinlschmidt;

Details and patient eligibility

About

The study assesses the potential of using computational models, specifically large language models, to simulate psychotherapeutic sessions, aiming to improve therapy outcomes and advance therapist training through innovative technology.

Full description

Health research has evolved significantly, increasingly incorporating computational models that improve our understanding and effectiveness of medical interventions. This shift from traditional to computational methods represents a major advancement in medical research, offering a more sustainable and innovative approach for conceptual advances and therapeutic discovery. In silico models, based on scientific simulation, use computational algorithms to mimic real-world systems or processes. This virtual environment allows researchers to explore phenomena impractical, unethical, dangerous, expensive, or impossible to study otherwise.

Psychotherapy is widely acknowledged as a primary treatment for a variety of mental health conditions, from depression and anxiety to personality disorders, offering significant pathways to recovery and improved quality of life. Yet current methods have shown limited effectiveness, prompting a need for innovative research approaches. In silico psychotherapy research leverages computational simulations, large language models (LLMs), and generative artificial intelligence to explore and refine psychotherapeutic interventions. By simulating human-like conversations, this approach provides insights into therapy dynamics and holds promise for revolutionizing therapist training and expanding treatment techniques.

This study aims to establish a proof-of-concept for simulating psychotherapeutic sessions using LLMs, focusing specifically on motivational interviewing. It involves the simulation of 512 psychotherapy sessions using LLMs as well as 8 real-world psychotherapy transcripts. By modeling human interactions, the study seeks to enhance healthcare delivery, therapist training, and personalized psychotherapy.

Enrollment: 520 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Simulation of psychotherapy sessions of conversations between an adult person presenting with a mental or behavioral health problem and a psychotherapist using large language models and 8 real-world transcripts

Exclusion criteria

• Simulation protocols with severe simulation errors

Trial design

Primary purpose: Other

Allocation: Non-Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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520 participants in 3 patient groups

High Levels of Common Therapeutic Factors

Experimental group

Description:

In this group, the patient-large language model (LLM) interacted with a therapist-LLM prompted to exhibit high levels of positive common factors.

Treatment:

Behavioral: High Levels of Common Therapeutic Factors

Low Levels of Common Therapeutic Factors

Experimental group

Description:

In this group, the patient-large language model (LLM) interacted with a therapist-LLM prompted to exhibit low levels of positive common factors.

Treatment:

Behavioral: Low Levels of Common Therapeutic Factors

Transcripts of real intervention sessions

Other group

Description:

This group consists of published transcripts of real intervention sessions, in which motivational interview techniques have been applied.

Treatment:

Behavioral: Standard motivational interviewing