Developing the theory and building multi-agent systems related to improving clinical processes for patients, clinicians, and hospitals.
Psychiatry involves complex, multi-step processes across patients, clinicians, and institutions. We develop theory and systems for multi-agent and generative AI workflows that can support those processes while remaining constrained, auditable, and aligned with clinical goals.
This includes agent architectures, process maps, and system designs that make AI assistance safer and more useful in psychiatric and behavioral health settings.
We are working on an agentic LLM chatbot that could help perform psychiatric intake. Our current work has been creating a simulated intake environment, where we can test our tool and existing LLMs, as well as compare them to clinician baselines. We are working alongside the field lab to create realistic synthetic patient vignettes. We also hope to develop education spinoffs from our work to support the next generation of psychiatrists.
Synthia Wang, King Shi, Amanda Li, Ariel Kim, Jonathan Ivey, Michelle Lu, Guan Gui
We are researching how AI and LLMs can help caregivers and clinicians make safer, patient-specific medication decisions for older adults, focusing on polypharmacy, mental health, and data gaps in geriatric psychiatric care. Our geriatric psychiatry work runs under this project.
Arushi Acharya, Peiyong Lin
