RadLE 2.0 shows medical AI models mask errors with false confidence
RadLE 2.0 benchmark reveals a critical failure mode in medical AI: models confidently deliver incorrect diagnoses without flagging uncertainty. This exposes a fundamental gap between raw accuracy and clinical safety. The finding underscores that AI deployment in high-stakes domains requires not just pattern recognition but calibrated confidence estimation and human-in-the-loop workflows. Until models learn to abstain when uncertain, radiologists remain the essential backstop, reshaping how the industry should think about AI-assisted diagnosis versus autonomous AI diagnosis.73
