Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry

Autor:	Hossain, Soaad, Rasalingam, James, Waheed, Arhum, Awil, Fatah, Kandiah, Rachel, Ahmed, Syed Ishtiaque
Rok vydání:	2024
Předmět:	Computer Science - Computers and Society Computer Science - Artificial Intelligence Computer Science - Computational Engineering Finance and Science Computer Science - Human-Computer Interaction Computer Science - Machine Learning 68T01 I.2 J.4 I.2.1 K.4.3
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, submitted to Machine Learning for Health (ML4H) 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.05856 Zobrazit plný text záznamu View this record from Arxiv