Reflection on the equitable attribution of responsibility for artificial intelligence-assisted diagnosis and treatment decisions

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices

“…The 73 included studies covered a broad spectrum of medical contexts, summarised with the main ethical issues raised and key findings in Supplementary Table S2. Briefly, the most frequently addressed areas were as follows: the general use of AI for healthcare (36 studies) , the use of AI in decision support systems (eight studies) [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], big data (four studies) [95][96][97][98], robotics (seven studies) [99][100][101][102][103][104][105] and adaptive AI (one study) [106]. The remaining studies addressed the following: rehabilitation [107], medical education [108], monitoring technology for the elderly [109], mental health [110], radiation technology [111], chatbots [112], health apps [113] and healthcare in low-and middleincome countries [114].…”

“…This was a key discussion point which was raised in depth in several explorations of transparency. Indeed, a critical and somewhat unique challenge raised by the nature of AI-driven technologies is the extent to which it is possible to understand how the underlying AI system works or reaches its output [49,60,62,64,66,70,85,86,88,92]. Many studies called for this 'explicability' or 'explainability' as a key principle to be met for AI health technologies [60,64], while others emphasised the need for contextual explainability [88].…”

“…The most frequently addressed ethical theme was transparency (n = 40) [44,46,47,49,[55][56][57][58][59][60][61][62]64,66,69,70,75,76,[78][79][80][81][82]85,86,[88][89][90]92,[94][95][96][99][100][101]107,110,111,113,114], concerning a wide range of medical contexts but focusing mainly in the area of clinical decision support. While the majority of studies reported a narrative evaluation of ethical issues, seven qualitative studies employed surveys or focus groups that identified transparency as an ethical priority by either healthcare professionals [44,78], experts in AI implementation [107], a combination of AI experts and citizens [65] or a combination of designers, clinician-users, administrators and patients, [69,70,…”

“…Trust was discussed as a theme in 34 of the included studies [42,50,52,54,[58][59][60][61][62][63][66][67][68]72,73,76,77,[79][80][81][82]86,87,89,90,93,94,101,102,105,107,110,112,113], with most focusing on clinical decision support systems, chatbots and robots. Most of these studies were articles or qualitative analyses.…”

Legal Protection from Artificial Intelligence Technology Used to Filter Visual Contents via the Internet

“…The extent and nature of the damage will determine which legal rules apply, such as product damage liability systems (Kingston, 2016), contractual liability rules, or even tort liability rules. In some cases, new frameworks may be created to establish a liability system that is suitable for the specific nature of artificial intelligence tools and their application field (Chen et al, 2022).…”