Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry

TL;DR

This paper investigates the economic impacts of implementing machine learning in clinical psychiatry, highlighting potential benefits, costs, and ethical considerations through case studies and economic evaluations.

Contribution

It fills a gap in literature by analyzing the economic implications and ethical considerations of ML in clinical psychiatry, using case studies and health economic evaluations.

Findings

Identifies economic benefits and costs of ML in psychiatry

Highlights ethical and legal considerations

Provides a framework for economic evaluation in clinical ML applications

Abstract

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.