Ethical Challenges in AI-based Clinical Decision Support System

Abstract

Clinical decision support systems are AI-based systems utilizing AI methods to assist the decision making process. In recent years, the use of clinical support systems has grown in popularity. In addition to its significant benefits in improving healthcare efficiency, it also raises several ethical questions. In this research, three case studies of real AI applications in the medical field have been investigated. The first one is PainChek®, an AI pain assessment tool utilized in Australian residential care. It is helpful for people, especially those who are not able to speak, to express their pain levels, but issues such as transparency, security, and accuracy are raised. The second example is IDx-DR, an FDA-approved automated method for identifying retinopathy in diabetic patients. Even while it can lower costs, there are still some issues with data management transparency, possible risks, and consequences on human specialists. The last case study discusses a hospital’s deployment of an AI-assisted end-of-life system. Although the system can boost physicians’ confidence and lower treatment costs by improving resource allocation, its shortcomings include bias and trust. To address those challenges, a framework has been developed for tracking ethical dilemmas that arise when developing machine learning models for medical applications. The framework combines policy regulation with model implementation processes. This framework aims to help the AI-based clinical system become more transparent and responsible, enhancing the effectiveness of clinical treatment as well as minimizing ethical risks.

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