Diagnostic accuracy differences in detecting wound maceration between humans and artificial intelligence: the role of human expertise revisited.

Abstract

This study aims to compare the diagnostic abilities of humans in wound image assessment with those of an AI-based model, examine how "expertise" affects clinicians' diagnostic performance, and investigate the heterogeneity in clinical judgments. A total of 481 healthcare professionals completed a diagnostic task involving 30 chronic wound images with and without maceration. A convolutional neural network (CNN) classification model performed the same task. To predict human accuracy, participants' "expertise," ie, pertinent formal qualification, work experience, self-confidence, and wound focus, was analyzed in a regression analysis. Human interrater reliability was calculated. Human participants achieved an average accuracy of 79.3% and a maximum accuracy of 85% in the formally qualified group. Achieving 90% accuracy, the CNN performed better but not significantly. Pertinent formal qualification (β  =  0.083, P < .001) and diagnostic self-confidence (β  =  0.015, P = .002) significantly predicted human accuracy, while work experience and focus on wound care had no effect (R2 = 24.3%). Overall interrater reliability was "fair" (Kappa = 0.391). Among the "expertise"-related factors, only the qualification and self-confidence variables influenced diagnostic accuracy. These findings challenge previous assumptions about work experience or job titles defining "expertise" and influencing human diagnostic performance. This study offers guidance to future studies when comparing human expert and AI task performance. However, to explain human diagnostic accuracy, "expertise" may only serve as one correlate, while additional factors need further research.