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Researchers from the University of Pennsylvania and New York University have received a $4 million grant from the Wellcome Trust to develop an AI-driven platform to train mental health clinicians using simulations of real patients. 

Within the next two years, the partners will work on the development of the STELLAR platform, which stands for Steering-Vector Enhanced LLM Agents for Realistic Digital Twins in Mental Health. The platform will create digital twins of patients that trainees can use to practice conducting clinical interviews and evaluating psychiatric symptoms. 

“STELLAR brings together behavioral data, clinical expertise, and AI to ask a very practical question,” said Sharath Chandra Guntuku, PhD, associate professor of computer and information science at Penn Engineering. “Can we build training tools that better prepare clinicians for how varied and complex patients are?”

Preparing future mental health clinicians for clinical interviews can be challenging as patients will often report overlapping symptoms that shift over time and subjective experiences that can be expressed differently by each individual. STELLAR will give trainees an ethical option for trainees to simulate interviewing patients with a broad range of symptoms, backgrounds, and clinical scenarios.

“In psychiatry, the details of symptom experience matter: how someone describes distress, how symptoms overlap, how severity changes over time, and how context shapes the clinical interaction,” said Raquel E. Gur, MD, PhD, professor of psychiatry, neurology, and radiology at Penn’s Perelman School of Medicine.

Patient simulations will be created drawing from clinical data from the Philadelphia Neurodevelopmental Cohort, a repository including psychiatric assessments and clinical interviews from thousands of young people created by Penn Medicine and the Children’s Hospital of Philadelphia. Rather than copying individual patients, the simulations will create composites based on real-world data for clinicians to practice realistic conversations in the context of a clinical interview. 

This will allow trainers to precisely control the symptoms students encounter, their intensity, and how they interact with each other. For instance, a trainee may practice interviewing a patient with mild anxiety and another whose anxiety overlaps with depression or psychosis to learn how to distinguish the differences in presentation between both.

Because many mental health symptoms manifest beyond formal clinical settings, the platform will also be trained using data from social media platforms, where people discuss mental health symptoms in everyday language.

“Patient simulations will only be useful for clinician training if they are grounded in real clinical speech and evaluated as clinical interactions, not just plausible AI dialogue,” said Neville Ryant, PhD, researcher at Penn’s Linguistic Data Consortium. “[Our] role is to bring speech and language science into the core of the project: adapting speech-recognition tools to clinical interviews, creating high-quality transcripts and annotations, and helping evaluate both what the simulations say and how they say it. That includes assessing the language generated by the models, the naturalness of synthetic voices, how well those voices reflect target speech patterns, and the behavior of the avatar during real trainee interactions.”

To ensure the conversations are realistic, respectful, and useful to trainees, the team will involve people with lived experience of mental health conditions as well as family members and caregivers to provide their perspective into the evaluation process. Their feedback will help researchers assess the accuracy of simulations, avoid stereotyping patients, and prepare trainees for complex and nuanced clinical conversations with real patients. 

“The promise of this approach is that we can move beyond stylized and potentially biased simulations,” said João Sedoc, PhD, assistant professor of technology, operations and statistics at NYU’s Stern School of Business. “If we can create digital patients that simulate controllable plausible symptom expression and responsibly evaluate, we can augment current clinician training practices with the kinds of conversations that are essential to better mental health care.”

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