The RadLE 2.0 benchmark reveals a concerning trend in AI models used for radiology, particularly in their ability to discern when to defer to human expertise. Many AI systems exhibit high confidence in their diagnostic capabilities, even when their findings are incorrect. This raises critical questions about the reliability of AI in medical contexts. Human radiologists continue to outperform these models, underscoring the necessity for AI to develop a more nuanced understanding of its limitations. Before AI can autonomously diagnose conditions, it must learn the importance of restraint and the value of human judgment. This situation highlights the broader implications of integrating AI into healthcare, where the balance between technological advancement and patient safety must be carefully managed.