Within the ethical debate on Machine Learning-driven decision support systems (ML_CDSS), notions such as “human in the loop” or “meaningful human control” are often cited as being necessary for ethical legitimacy. In addition, ethical principles usually serve as the major point of reference in ethical guidance documents, stating that conflicts between principles need to be weighed and balanced against each other. Starting from a neo-Kantian viewpoint inspired by Onora O'Neill, this article makes a concrete suggestion of how to interpret the (...) role of the “human in the loop” and to overcome the perspective of rivaling ethical principles in the evaluation of AI in health care. We argue that patients should be perceived as “fellow workers” and epistemic partners in the interpretation of ML_CDSS outputs. We further highlight that a meaningful process of integrating (rather than weighing and balancing) ethical principles is most appropriate in the evaluation of medical AI. (shrink)

The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing (...) a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns. (shrink)

Early and accurate diagnosis of glaucoma is critical for avoiding human vision deterioration and preventing blindness. A deep-neural-network model has been developed for the diagnosis of glaucoma based on Heidelberg retina tomography, called “Seeking Common Features and Reserving Differences Net” to make full use of the HRT data. In this work, the proposed SCRD-Net model achieved an area under the curve of 94.0%. For the two HRT image modalities, the model sensitivities were 91.2% and 78.3% at specificities of 0.85 and (...) 0.95, respectively. These results demonstrate a significant improvement over earlier results. In addition, we visualized the network outputs to develop an interpretation of the learned mechanism for discriminating glaucoma and normal images. Thus, the SCRD-Net can be an effective diagnostic indicator of glaucoma during clinical screening. To facilitate SCRD-Net utilization by the scientific community, the code implementation will be made publicly available. (shrink)