Lee, E. E., Torous, J. et al. (2021).
Biological Psychiatry Cognitive
Neuroscience and Neuroimaging, 6(9), 856–864.
Abstract
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.
Here are some thoughts:
The paper explores AI’s potential in mental health, where its adoption has lagged behind other medical fields due to challenges like data sensitivity, diagnostic complexity, and ethical concerns. AI applications, including machine learning (ML) and natural language processing (NLP), have demonstrated promise in diagnosing mental illnesses, predicting suicide risk, and personalizing treatments using data from electronic health records (EHRs), brain imaging, wearable sensors, and social media. AI has also been explored for optimizing psychiatric treatments by predicting patient responses to antidepressants, cognitive behavioral therapy (CBT), and electroconvulsive therapy. However, there are major obstacles, including the lack of FDA-approved AI applications in psychiatry, concerns about biased training data, and challenges in integrating AI into clinical practice. Programs like REACH VET, which identifies veterans at high risk for suicide, show AI’s potential, but widespread adoption requires overcoming clinician skepticism and ensuring AI models are transparent and equitable.
The concept of Artificial Wisdom (AW) is introduced as an evolution of AI that goes beyond intelligence to incorporate ethical decision-making, empathy, and fairness. While AI can process vast amounts of data, it lacks human wisdom, which is essential for compassionate and just mental healthcare. The paper argues that future AI should not only improve efficiency but also align with human values to ensure patient well-being. This will require collaboration among computer scientists, psychiatrists, and ethicists to develop unbiased, transparent AI models governed by strong regulatory frameworks. Ultimately, AI should complement clinicians by automating administrative tasks and enhancing diagnostic accuracy, allowing professionals to focus on patient relationships. If ethical and technical challenges are addressed, AI and AW have the potential to transform mental healthcare, making it more personalized, efficient, and equitable.