Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review
Artificial intelligence diagnostic tools (AIDTs) in oncology show high image classification accuracy but limited clinical adoption. Their adoption could be enhanced by (i) using user feedback during the software design, (ii) demonstrating that AIDTs improve the user’s decisions, and (iii) providing explanations of AI decisions tailored to the user, three aspects central to human-centered AI (HCAI). This review assesses these three aspects in AIDTs for oncology in general, exemplifying its concepts in the established field of skin cancer diagnostics as a specific use case.
