分析机器学习在精神科临床中的经济影响，填补医疗AI经济学研究空白。
Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry
- 通过三个案例研究和两类健康经济评估方法进行实证分析
- 揭示机器学习应用在精神科临床中的成本效益与潜在经济风险
- 兼顾公平性、法律与伦理考量，适合政策制定者参考
随着人工智能和机器学习（ML）在医学领域的兴起，相关研究日益关注其在临床精神病学中的应用与伦理问题。然而，关于使用机器学习在精神科临床中所伴随的经济影响的研究仍十分匮乏。本文旨在填补这一空白，通过三个面向具体问题的案例研究、对经济学、社会经济与医疗人工智能文献的梳理，以及两种类型的健康经济评价，系统评估机器学习在临床精神病学中的经济影响。同时，论文还详细讨论了机器学习在精神科应用中的公平性、法律、伦理及其他相关考虑因素。
原文摘要 · Abstract (English)
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.
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