An article highlights rapid progress in deploying AI to interpret medical images, with a new review focusing on deep learning approaches that connect imaging inputs with clinical decision-making. It addresses both the promise—automation of pattern recognition—and the operational risks that can emerge when models encounter data outside training distributions. The report frames key practical issues for implementation, including validation, oversight, and the need for reliable performance in noisy real-world settings. It also reflects how medical imaging AI is increasingly moving from retrospective benchmarks toward prospectively tested clinical systems. For biotech and health tech stakeholders, the signal is that imaging AI is accelerating, but deployment requirements are tightening—placing pressure on study design, monitoring, and governance.