Large Language Models Versus Expert Clinicians in Crisis Prediction Among Telemental Health Patients: Comparative Study.
Christine LeeMatthew MohebbiErin O'CallaghanMirene WinsbergPublished in: JMIR mental health (2024)
GPT-4, with a simple prompt design, produced results on some metrics that approached those of a trained clinician. Additional work must be done before such a model can be piloted in a clinical setting. The model should undergo safety checks for bias, given evidence that LLMs can perpetuate the biases of the underlying data on which they are trained. We believe that LLMs hold promise for augmenting the identification of higher-risk patients at intake and potentially delivering more timely care to patients.
Keyphrases
- end stage renal disease
- healthcare
- ejection fraction
- chronic kidney disease
- public health
- newly diagnosed
- palliative care
- peritoneal dialysis
- patient reported outcomes
- machine learning
- physical activity
- quality improvement
- body mass index
- pain management
- chronic pain
- artificial intelligence
- climate change
- weight loss
- health promotion