Autonomous reporting of 'normal' chest X-rays by artificial intelligence in the United Kingdom; can we take the human out of the loop?

Chest X-rays (CXRs) are the most commonly performed imaging investigation. In the UK, many centers experience reporting delays due to radiologist workforce shortages. Artificial intelligence (AI) tools capable of distinguishing "normal" from "abnormal" CXRs have emerged as a potential solution. If "normal" CXRs could be safely identified and reported without human input, a substantial portion of radiology workload could be reduced. This article examines the feasibility and implications of autonomous AI reporting of "normal" CXRs, using the United Kingdom as an example setting. Key issues include defining "normal," ensuring generalizability across populations, and managing the sensitivity-specificity trade-off. It also addresses legal and regulatory challenges, such as compliance with IR(ME)R and GDPR, and the lack of accountability frameworks for errors. Further considerations include the impact on radiologists practice, the need for robust post-market surveillance, and incorporation of patient perspectives. While the benefits are clear, adoption must be cautious, with strong governance, legal clarity, and rigorous clinical validation to ensure safe and sustainable use.