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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.13428 |
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| _version_ | 1866909797047074816 |
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| author | Nash, Katrina Vaz, James Maiter, Ahmed Johns, Christopher Woznitza, Nicholas Kale, Aditya Morgado, Abdala Espinosa Bramley, Rhidian Hall, Mark Lowe, David Novak, Alex Ather, Sarim |
| author_facet | Nash, Katrina Vaz, James Maiter, Ahmed Johns, Christopher Woznitza, Nicholas Kale, Aditya Morgado, Abdala Espinosa Bramley, Rhidian Hall, Mark Lowe, David Novak, Alex Ather, Sarim |
| contents | Chest X-rays (CXRs) are the most commonly performed imaging investigation. In the UK, many centres 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. Key issues include defining normal, ensuring generalisability 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13428 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomous Reporting of Normal Chest X-rays by Artificial Intelligence in the United Kingdom. Can We Take the Human Out of the Loop? Nash, Katrina Vaz, James Maiter, Ahmed Johns, Christopher Woznitza, Nicholas Kale, Aditya Morgado, Abdala Espinosa Bramley, Rhidian Hall, Mark Lowe, David Novak, Alex Ather, Sarim Populations and Evolution Computer Vision and Pattern Recognition Image and Video Processing Chest X-rays (CXRs) are the most commonly performed imaging investigation. In the UK, many centres 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. Key issues include defining normal, ensuring generalisability 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 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. |
| title | Autonomous Reporting of Normal Chest X-rays by Artificial Intelligence in the United Kingdom. Can We Take the Human Out of the Loop? |
| topic | Populations and Evolution Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2509.13428 |