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DTSTART:20241103T020000
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DTSTART:20250309T020000
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DESCRIPTION:Title: Diagnostic Accuracy and Clinical Reasoning of Multiple 
 Large Language Models Abstract: Large language models are increasingly us
 ed for mental health–related questions\, yet their performance in psychia
 try - where diagnosis depends heavily on narrative interpretation and cli
 nical reasoning - remains poorly understood. In this talk\, I’ll present 
 a mixed-methods evaluation of four contemporary LLMs on 196 psychiatric c
 ase vignettes\, combining large-scale diagnostic accuracy metrics with cl
 inician-rated assessments of diagnostic reasoning. We find that models ca
 n achieve high diagnostic accuracy on vignettes\, but - crucially - that 
 clinician-rated reasoning quality is far more predictive of diagnostic co
 rrectness than surface-level data extraction. These findings suggest that
  evaluating how models reason\, not just what they predict\, is essential
  for understanding their potential role in psychiatric decision support. 
 Kevin Jin is a third-year PhD student in the Interdepartmental Program in
  Computational Biology and Biomedical Informatics at Yale University. He 
 is advised by Hua Xu in the Clinical NLP Lab\, a research group in the De
 partment of Biomedical Informatics and Data Science at Yale School of Med
 icine. He completed his undergraduate work at Johns Hopkins University\, 
 receiving a B.S. in Molecular and Cellular Biology in 2020. He is support
 ed by the NSF Graduate Research Fellowship.\n\nSpeaker:\nKevin Jin\n\nAdm
 ission:\nFree\n\nDetails URL:\nhttps://medicine.yale.edu/event/nlpllm-int
 erest-group-22/\n
DTEND;TZID=America/New_York:20260209T170000
DTSTAMP:20260503T013830Z
DTSTART;TZID=America/New_York:20260209T160000
LOCATION:Zoom link and passcode will be share on an email
SEQUENCE:0
STATUS:Confirmed
SUMMARY:NLP/LLM Interest Group
UID:c5c1a824-f892-43e4-82ec-9f721b86b62e
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