OBJECTIVES: This review characterised how IC is justified and operationalised for AI; synthesised ethical, legal, governance, and practical requirements; identified gaps in consent models, stakeholders, and AI functionality; and developed author-derived communication thresholds for notification, routine clinical consent with explicit AI disclosure, or AI-specific IC.
METHODS: We conducted a PRISMA-ScR-guided scoping review with an OSF-registered protocol. MEDLINE, Scopus, IEEE Xplore, arXiv, Google Scholar, Web of Science, and HeinOnline were searched for English-language sources published 2015 to 25 May 2026. From 6,242 records, 116 reports were assessed; 69 were included, plus one manual source, yielding 70. Data were charted across 24 domains, synthesised, and appraised with JBI tools.
RESULTS: Publications peaked in 2024 (22/70, 31.4%). The evidence base was non-empirical: conceptual analyses (37/70, 52.9%) and narrative reviews/book chapters (17/70, 24.3%). Medicine-only sources predominated (60/70, 85.7%); dentistry-only sources accounted for 8/70 (11.4%). Traditional IC appeared alone in 46/70 sources (65.7%) and overall in 52/70 (74.3%); dynamic consent was uncommon (6/70, 8.6%). IC was endorsed in 67/70 (95.7%) and qualified in 40/70 (57.1%). Explainability/transparency was addressed in 65/70 (92.9%), and proposed solutions in 57/70 (81.4%), but formal protocols remained uncommon (6/70, 8.6%). Thresholds consolidated rules by AI application, automation, risk, data use, and patient decision relevance.
CONCLUSIONS: AI-related IC is widely endorsed but remains fragmented and largely conceptual. Findings support a risk-adaptive approach to AI-informed consent, calibrated to AI function, automation, risk, data use, explainability, and clinical decision impact. The author-derived thresholds offer a synthesis-informed basis for future governance guidance or framework development, pending empirical and stakeholder validation.