SynthesisHealth technology assessment (Winchester, England)2026
Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.
Synthesis in Health technology assessment (Winchester, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of artificial intelligence in medical image classification for screening has the potential to enhance efficiency, diagnostic accuracy and accessibility. However, ethical concerns such as accountability, bias, transparency and the impact on healthcare professionals remain critical. This review synthesises qualitative evidence on the ethical considerations surrounding artificial intelligence adoption in screening programmes. Methods: A systematic search of qualitative studies, from June 2020 to September 2024, was conducted across multiple databases: MEDLINE, EMBASE, PsycInfo Results: Fourteen qualitative studies were included, covering perspectives from clinicians, radiologists, artificial intelligence developers, policy-makers and patients. Key ethical concerns identified included: (1) the necessity of human oversight to ensure that artificial intelligences diagnostic recommendations are appropriate; (2) challenges in assigning liability when artificial intelligence errors occur; (3) risks of algorithmic bias due to discrepancies between training data sets and real-world populations; (4) concerns over data privacy, cybersecurity and informed consent in artificial intelligence-driven decision-making; (5) the need for transparency in artificial intelligence decision-making processes to build trust and (6) potential deskilling of healthcare professionals and shifts in professional responsibilities. While artificial intelligence was seen as a valuable tool to augment clinical decision-making, stakeholders emphasised that ethical frameworks must guide its implementation to maintain public trust and patient safety. Conclusion: This review highlights the critical considerations that must be addressed to ensure the responsible integration of artificial intelligence in medical screening. Policy-makers, healthcare institutions and developers should prioritise human oversight, robust regulatory frameworks and strategies to mitigate bias and ensure transparency. Future research should focus on disease-specific artificial intelligence applications and long-term ethical implications. Study registration: The protocol for this study is registered on PROSPERO as CRD42024599536. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR172233) and is published in full in
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