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
- mental illness
- end stage renal disease
- depressive symptoms
- artificial intelligence
- chronic kidney disease
- ejection fraction
- newly diagnosed
- sleep quality
- endothelial cells
- climate change
- human health
- primary care
- emergency department
- peritoneal dialysis
- patient reported
- patient reported outcomes
- quality improvement
- pluripotent stem cells
- bone marrow
- health information
- replacement therapy
- electronic health record