The ethical adoption of artificial intelligence in radiology
Open accessAbstract
Artificial intelligence (AI) is rapidly transforming healthcare—with radiology at the pioneering forefront. To be trustfully adopted, AI needs to be lawful, ethical and robust. This article covers the different aspects of a safe and sustainable deployment of AI in radiology during: training, integration and regulation. For training, data must be appropriately valued, and deals with AI companies must be centralized. Companies must clearly define anonymization and consent, and patients must be well-informed about their data usage. Data fed into algorithms must be made AI-ready by refining, purification, digitization and centralization. Finally, data must represent various demographics. AI needs to be safely integrated with radiologists-in-the-loop: guiding forming concepts of AI solutions and supervising training and feedback. To be well-regulated, AI systems must be approved by a health authority and agreements must be made upon liability for errors, roles of supervised and unsupervised AI and fair workforce distribution (between AI and radiologists), with a renewal of policy at regular intervals. Any errors made must have a root-cause analysis, with outcomes fedback to companies to close the loop—thus enabling a dynamic best prediction system. In the distant future, AI may act autonomously with little human supervision. Ethical training and integration can ensure a "transparent" technology that will allow insight: helping us reflect on our current understanding of imaging interpretation and fill knowledge gaps, eventually moulding radiological practice. This article proposes recommendations for ethical practise that can guide a nationalized framework to build a sustainable and transparent system.
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- Authors
- Mudgal, K. S., & Das, N.
- Affiliations
- King's College Hospital NHS Foundation Trust (GB)
- East Kent Hospitals Foundation Trust, Canterbury, UK
- Corresponding author
- Mudgal, Keshav Shree
- ORCID
- Mudgal, Keshav Shree
- Venue
- BJR|Open
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11-01
- Volume
- 2
- Issue
- 1
- Pages
- 20190020
- ISSN
- 2513-9878 (ISSN-L)
- PMC
- PMC7605209
- OpenAlex
- W2991302197
- Cited by
- 52
- References
- 17
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 13 · 2023: 10 · 2022: 8 · 2021: 7 · 2020: 2
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BJR|Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- artificial intelligence in radiology, AI ethics, AI regulation, informed consent, data quality, demographic representativeness, liability for AI errors, root cause analysis, AI governance, AI autonomy
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