Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.
Zohar ElyosephZohar ElyosephShiri Shinan-AltmanPublished in: Family medicine and community health (2024)
This study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology's prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients' drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.
Keyphrases
- mental health
- healthcare
- end stage renal disease
- mental illness
- depressive symptoms
- artificial intelligence
- newly diagnosed
- ejection fraction
- endothelial cells
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- sleep quality
- human health
- primary care
- emergency department
- machine learning
- stem cells
- patient reported outcomes
- quality improvement
- risk assessment
- bone marrow
- physical activity
- patient reported