Human-AI Collaboration in Radiology: The Blind Spots.
TL;DR
A narrative synthesis of the human-AI interaction and radiology AI literature highlighted three underrecognized determinants of successful human-AI collaboration in radiology, and concrete research directions are proposed to bridge the gap between algorithmic capabilities and clinical utility.
Abstract
Research on AI in diagnostic radiology has focused on algorithm development and standalone performance, yet the human-AI interactions that ultimately determine the clinical value of AI are poorly understood. A narrative synthesis of the human-AI interaction and radiology AI literature highlighted three underrecognized determinants of successful human-AI collaboration in radiology. First, cognitive psychology dictates how automation bias, the framing of AI uncertainty, and AI-induced skill decay distort diagnostic reasoning. Second, the user interface (UI) and user experience (UX) of AI tools determine how the timing, salience, and documentation of AI findings shape radiologists' attention and reporting behavior; the influence of UI/UX is becoming even more critical as generative AI introduces novel applications and interaction paradigms. Third, algorithmic conformity leads radiologists to override their own judgment under medicolegal, transparency, and organizational pressures. For each domain, concrete research directions are proposed to bridge the gap between algorithmic capabilities and clinical utility.
