Is autonomous AI safe?

AI has swept across nearly every domain and nowhere does it hold more transformative potential than in healthcare. Among medical specialities, radiology stands out as particularly well suited for AI innovation owing to early digitalisation and the abundance of rich data that provide ideal inputs for machine learning models. Radiology accounts for approximately 80% of all FDA-approved AI medical devices to date – a striking testament to how well imaging tasks map to AI capabilities. Many of these tasks, such as detecting abnormalities, classifying disease or measuring anatomical structures, translate naturally into computer vision problems, making AI-driven solutions both technically feasible and clinically valuable.

The promise is compelling, ranging from alleviating workforce shortages and reducing workload pressures, to ultimately improving patient outcomes and streamlining care. However, as the technology has evolved, so too has the discussion around its safety, with persisting concerns on its robustness and adaptability to dynamic clinical environments, the risks of algorithmic bias and the implications of shifting decision making away from radiologists. This article explores these key considerations, with a particular focus on autonomous AI systems in radiology – the opportunities they present, the challenges they pose and the path forward.

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