Ethical Challenges of Artificial Intelligence in Medicine and the Triple Semantic Dimensions of Algorithmic Opacity with Its Repercussions to Patient Consent and Medical Liability

Abstract

AbstractArtificial intelligence algorithms have the potential to diagnose some types of skin cancer or to identify specific heart-rhythm abnormalities as well as (or even better) than board-certified dermatologists and cardiologists. However, one of the biggest fears in the healthcare sector in the Era of AI in Medicine is the so-called black box medicine, given the obscurity in the way information is processed by algorithms. More broadly, it is observed that there are three different semantic dimensions of algorithmic opacity relevant to Medicine: (1) epistemic opacity for the insufficient physicians understanding of the rules an AI system is applying to make predictions and decisions; (2) opacity for the lack of medical disclosure about the AI systems to support clinical decisions and patient’s unawareness that automated decision-making are being carried out with their personal data; (3) explanatory opacity for the unsatisfactory explanation to patients about the technology used to support professional decision-making. Therefore, the aim of this study is to analyze each type of opacity, considering hypothetical scenarios and its repercussions in terms of medical malpractice and patient’s informed consent. From this, it will be defined ethical challenges of using AI in the healthcare sector and the importance of medical education.