AI in imaging: the regulatory landscape
Open accessAbstract
Artificial intelligence (AI) methods have been applied to medical imaging for several decades, but in the last few years, the number of publications and the number of AI-enabled medical devices coming on the market have significantly increased. While some AI-enabled approaches are proving very valuable, systematic reviews of the AI imaging field identify significant weaknesses in a significant proportion of the literature. Medical device regulators have recently become more proactive in publishing guidance documents and recognizing standards that will require that the development and validation of AI-enabled medical devices need to be more rigorous than required for tradition “rule-based” software. In particular, developers are required to better identify and mitigate risks (such as bias) that arise in AI-enabled devices, and to ensure that the devices are validated in a realistic clinical setting to ensure their output is clinically meaningful. While this evolving regulatory landscape will mean that device developers will take longer to bring novel AI-based medical imaging devices to market, such additional rigour is necessary to address existing weaknesses in the field and ensure that patients and healthcare professionals can trust AI-enabled devices. There would also be benefits in the academic community taking into account this regulatory framework, to improve the quality of the literature and make it easier for academically developed AI tools to make the transition to medical devices that impact healthcare.
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Social bias arises from societal inequalities and prejudices embedded in the data.
“Social bias arises from societal inequalities and prejudices embedded in the data [13,29,48].”
From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [48]
The Discussion’s Bias section says imaging datasets can be demographically skewed and describes potential ethnic and socio-economic biases that may hamper generalization. That supports the data-bias aspect of the claim, but the paper does not specifically say those biases arise from societal inequalities or prejudices embedded in the data. No page number is available in our copy. (LLM, conf=0.82)
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Cites
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Sorted by citations
Amaya-Santos, S., Vargas, C., & Bermúdez-Tamayo, C. (2026) · Public Health in Practice · doi:10.1016/j.puhip.2025.100713 · Cited by 4 · see citing works
“Social bias arises from societal inequalities and prejudices embedded in the data [13,29,48].”
- Authors
- Hill, D. L. G.
- Affiliations
- UCL , Gower Street , London, WC1E 6BT, United Kingdom
- ORCID
- Hill, Derek L G
- Venue
- British Journal of Radiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-01-04
- Volume
- 97
- Issue
- 1155
- Pages
- 483-491
- ISSN
- 0007-1285 (ISSN-L)
- 1748-880X
- PMC
- PMC11027239
- OpenAlex
- W4390590287
- Cited by
- 52
- References
- 5
- Citations by year
- 2026: 23 · 2025: 24 · 2024: 5
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- British Journal of Radiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Healthcare Regulation
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- artificial intelligence, diagnostic imaging, regulatory guidelines, AI-enabled medical devices, device validation, risk mitigation, algorithmic bias, clinical meaningfulness, regulatory standards
- MeSH terms
- Artificial Intelligence, Health Personnel, Humans, Publishing, Software
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