How can artificial intelligence and large language models help more people get science-backed treatments for mental disorders? How do psychological processes like depression and anxiety show up in human language? I use large language models, natural language processing, and machine learning to address these questions.
I am a computational clinical psychologist and postdoc at the Stanford Institute for Human-Centered AI working in the Computational Psychology and Well-Being and Fidelity, Adaptation, Sustainability, and Training labs. I did my PhD at the University of Pennsylvania in the Boundaries of Anxiety and Depression lab. I’m part of the World Well-Being Project. My research has been supported by the National Science Foundation.
I am trained in evidence-based therapies, including CBT for depression and insomnia, exposure therapy for the anxiety disorders, exposure and response prevention for OCD, and cognitive processing therapy and prolonged exposure for PTSD.
I write about the future of AI and mental heath, including how large language models can improve research and treatment, what needs to happen to do this effectively, and how we will know when these applications are ready to deploy.
Stade, E. C., Eichstaedt, J. E., Kim, J. P., & Wiltsey Stirman, S., (in press). Readiness Evaluation for AI Deployment and Implementation for Mental Health: A review and framework. Technology, Mind, and Behavior.
Stade, E. C., Wiltsey Stirman, S., Ungar, L., Boland, C. L., Schwartz, H. A., Yaden, D. B., Sedoc, J., DeRubeis, R. J., Willer, R., Eichstaedt, J. E. (2024). Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. npj Mental Health Research.
Stade, E. C., Wiltsey Stirman, S., Held, P., Schwartz, H. A., & Eichstaedt, J. E., (preprint). Designing clinical psychological AI that reduces suffering: Challenges and technical considerations.
I also develop products that use large language models to improve assessment and treatment of psychopathology, including an AI-based CBT homework helper and an AI-based platform for training therapists in gold-standard treatments:
Stade, E. C., Eichstaedt, J. E., Kaysen, D. Salecha, A., Greenberger, A., Singhbi, S., & Wiltsey Stirman, S., (preprint). TherapyTrainer: Using AI to train therapists in written exposure therapy.
Ganesan, A. V., Varadarajan, V., Lal, Y. K., Eijsbroek, V. C., Kjell, K., Kjell, O. N. E., Dhanasekaran, T., Stade, E. C., Eichstaedt, J. C., Boyd, R. L., Schwartz, H. A., & Flek, L. (preprint). Explaining GPT-4’s schema of depression using machine behavior analysis.
I have also used natural language processing to characterize and measure (and disentangle!) depression and anxiety and repetitive negative thinking. Some of my current work investigates what drives the relationship between depression and first-person singular pronoun use (e.g., I, me, mine).
Rai, S., Stade, E. C., Giorgi, S., Francisco, A., Ungar, L., Curtis, B., Guntuku, S. C. (2024). Key language markers of depression depend on race. PNAS.
Stade, E. C., Ungar, L., Eichstaedt, J. C., Sherman, G., Ruscio, A. M. (2023). Depression and anxiety have distinct and overlapping language patterns: Results from a clinical interview. Journal of Psychopathology and Clinical Science.
Stade, E. C., Ungar, L., Havaldar, S., Ruscio, A. M. (2023). Perseverative thinking is associated with features of spoken language. Behaviour Research and Therapy.
Beyond these research streams, I also use empirical methods to study cognitive and behavioral processes, like worry, rumination, perseverative thinking, and behavioral anxiety.
Stade, E. C., & Ruscio, A. M. (2022). A meta-analysis of the relationship between worry and rumination. Clinical Psychological Science.
Stephenson, A. R., Stade, E. C., & Ruscio, A. M. (in press). Measuring behavioral responses to a social stressor: Does the Social Performance Rating Scale have utility beyond social anxiety disorder? Behaviour Research and Therapy.
Stade, E. C., Cohen, R., Loftus, P., & Ruscio, A. M. (2022). A novel measure of real-time perseverative thought. Clinical Psychological Science. 10(3), 534–552.
Depression and anxiety are not categorical (they are dimensional – severity matters) and cut across traditional diagnostic classes (they are transdiagnostic). The prevailing classification system, DSM-5, is neither transdiagnostic nor dimensional. This is a problem! We developed and tested a transdiagnostic, dimensional anxiety classification system as an alternative.
Stade, E. C., DeRubeis, R. J., Ungar, L., & Ruscio, A. M. (2023). A transdiagnostic, dimensional classification of anxiety shows improved parsimony and predictive noninferiority to DSM. Journal of Psychopathology and Clinical Science.
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