Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles

Artificial intelligence (AI) methods have great potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients. AI based computer-assisted diagnosis tools can have a tremendous benefit if they can outperform or perform similarly to the level of a clinical expert. As a result, advanced healthcare services can be affordable in developing nations, and the problem of a lack of expert medical practitioners can be addressed. AI based tools can save time, resources, and overall cost for patient treatment. Furthermore, in contrast to humans, AI can uncover complex relations in the data from a large set of inputs and even lead to new evidence-based knowledge in medicine. However, integrating AI in healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility and accountability, which must be addressed before integrating such tools into clinical settings. In this article, we emphasize recent advances in AI-assisted medical image analysis, existing standards, and the significance of comprehending ethical issues and best practices for the applications of AI in clinical settings. We cover the technical and ethical challenges of AI and the implications of deploying AI in hospitals and public organizations. We also discuss promising key measures and techniques to address the ethical challenges, data scarcity, racial bias, lack of transparency, and algorithmic bias. Finally, we provide our recommendation and future directions for addressing the ethical challenges associated with AI in healthcare applications, with the goal of deploying AI into the clinical settings to make the workflow more efficient, accurate, accessible, transparent, and reliable for the patient worldwide.

Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or not available. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide a highly innovative algorithm to quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. Inspired by the information bottleneck concept, we mask the neural network representation with noise to find out important regions. This approach overcomes the drawbacks of commonly used Grad-Cam and its derived algorithms. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable and robust severity estimation than the similar attribution methods.