Generative artificial intelligence in mental health care: potential benefits and current challenges

Abstract

Generative artificial intelligence in mental health care: potential benefits and current challengesThe potential of artificial intelligence (AI) in health care is being intensively discussed, given the easy accessibility of programs such as ChatGPT.While it is usually acknowledged that this technology will never replace clinicians, we should be aware of imminent changes around AI supporting: a) routine office work such as billing, b) clinical documentation, c) medical education, and d) routine monitoring of symptoms.These changes will likely happen rapidly.In summer 2023, the largest electronic medical records provider in the US, Epic Systems, announced that it is partnering with OpenAI to integrate ChatGPT technology 1 .The profound impact that these changes will have on the context and delivery of mental health care warrants attention, but often overlooked is the more fundamental question of changes to the nature of mental health care in terms of improving prevention, diagnosis and treatments.Research on non-clinical samples suggests that AI may augment text-based support programs, but assessments have focused on perceived empathy rather than clinical outcomes.While the former is an important development, it is only the first step towards progressing from feasibility to acceptability and from efficacy to effectiveness.A century of accessible self-help books, more than 60 years of mental health chatbots (Eliza was created in 1959), nearly 30 years of home Internet with access to free online cognitive behavioral therapy and chatrooms, over a decade of smartphone-based mental health apps and text message support programs, and the recent expansion of video-based telehealth, together highlight that access to resources is not a panacea for prevention.The true target for AI preventive programs should not be replicating previous work but rather developing new models able to provide personalized, environmentally and culturally responsive, and scalable support that works effectively for users across all countries and regions.Computer-based diagnosis programs have existed for decades and have not transformed care.Many studies to date suggest that new AI models can diagnose mental health conditions in the context of standardized exam questions or simple case examples 2 .This is important research, and there is evidence of improvement with new models, but the approach belies the clinical reality of how diagnosis is made or utilized in clinical care.The future of diagnosis in the 21st century can be more inclusive, draw from diverse sources of information, and be outcomes-driven.The true target for AI programs will be to integrate information from clinical exam, patient self-report, digital phenotyping, genetics, neuroimaging, and clinical judgement into novel diagnostic categories that may better reflect the underlying nature of mental illness and offer practical value in guiding effective treatments and cures.Currently, there is a lack of evidence about how AI programs can guide mental health treatment.Impressive studies show that AI can help select psychiatric medications 3 , but these studies often rely on complete and labelled data sets, which is not the clinical

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