Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent? - Ursin, Frank; Timmermann, Cristian; Orzechowski, Marcin; Steger, Florian (Frontiers in Medicine, 2021)
Purpose: Diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems is commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure. Results: The narrow search yielded n=537 records of which n=4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n=60 records of which n=11 were included. Eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI. Conclusions: Implications for the general practitioner are twofold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient’s overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.