Artificial intelligence systems are quickly becoming part of clinical diagnostics in areas like imaging, pathology, genomics, and point-of-care testing. They promise to improve the speed, accuracy, and availability of easy diagnoses. However, integrating them presents significant ethical and legal challenges. Key ethical concerns include algorithmic bias, lack of transparency, known as the black box problem, threats to patient autonomy and consent, data privacy, and trust issues. Legal issues focus on assigning liability, ensuring that regulations fit adaptive algorithms, complying with data protection across different regions, intellectual property rights, and managing cross-border governance. This article highlights common failure modes and suggests practical steps for deploying artificial intelligence diagnostics in an ethical and legal manner. These steps include developing regulatory pathways, requiring bias audits, establishing explainable standards, monitoring throughout the lifecycle, and clarifying liability frameworks.
Sciences` et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: