Ethical and legal considerations influencing human involvement in the implementation of artificial intelligence in a clinical pathway: A multi-stakeholder perspective.

Abstract

Ethical and legal factors will have an important bearing on when and whether automation is appropriate in healthcare. There is a developing literature on the ethics of artificial intelligence (AI) in health, including specific legal or regulatory questions such as whether there is a right to an explanation of AI decision-making. However, there has been limited consideration of the specific ethical and legal factors that influence when, and in what form, human involvement may be required in the implementation of AI in a clinical pathway, and the views of the wide range of stakeholders involved. To address this question, we chose the exemplar of the pathway for the early detection of Barrett's Oesophagus (BE) and oesophageal adenocarcinoma, where Gehrung and colleagues have developed a "semi-automated", deep-learning system to analyse samples from the CytospongeTM TFF3 test (a minimally invasive alternative to endoscopy), where AI promises to mitigate increasing demands for pathologists' time and input. We gathered a multidisciplinary group of stakeholders, including developers, patients, healthcare professionals and regulators, to obtain their perspectives on the ethical and legal issues that may arise using this exemplar. The findings are grouped under six general themes: risk and potential harms; impacts on human experts; equity and bias; transparency and oversight; patient information and choice; accountability, moral responsibility and liability for error. Within these themes, a range of subtle and context-specific elements emerged, highlighting the importance of pre-implementation, interdisciplinary discussions and appreciation of pathway specific considerations. To evaluate these findings, we draw on the well-established principles of biomedical ethics identified by Beauchamp and Childress as a lens through which to view these results and their implications for personalised medicine. Our findings are not only relevant to this context but have implications for AI in digital pathology and healthcare more broadly.