Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry
It addresses the economic implications of ML in clinical psychiatry for researchers and practitioners, but it is incremental as it fills a gap in existing literature without introducing new methods or broad SOTA results.
This study tackles the lack of literature on the economic aspects of using machine learning in clinical psychiatry by evaluating its economic implications through case studies, literature reviews, and health economic evaluations, while also addressing fairness, legal, and ethical considerations.
With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry.