{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:20:57Z","timestamp":1792794057500,"version":"4.5.0"},"reference-count":89,"publisher":"National Institute for Health and Care Research","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000664","name":"Health Technology Assessment programme","doi-asserted-by":"publisher","award":["NIHR172233"],"award-info":[{"award-number":["NIHR172233"]}],"id":[{"id":"10.13039\/501100000664","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journalslibrary.nihr.ac.uk"],"crossmark-restriction":false},"short-container-title":["Health Technol Assess"],"abstract":"<jats:sec id=\"abs1-1\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs1-2\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A systematic search of qualitative studies, from June 2020 to September 2024, was conducted across multiple databases: MEDLINE, EMBASE, PsycInfo\n                      <jats:sup>\u00ae<\/jats:sup>\n                      (American Psychological Association, Washington, DC, USA) and Cumulative Index to Nursing and Allied Health Literature. Primary qualitative studies exploring healthcare professionals\u2019, patients\u2019 and other stakeholders\u2019 perspectives on artificial intelligence in screening were included. Thematic analysis was performed, and findings were assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach to evaluate confidence in the evidence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs1-3\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs1-4\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs1-5\">\n                    <jats:title>Study registration<\/jats:title>\n                    <jats:p>The protocol for this study is registered on PROSPERO as CRD42024599536.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs1-6\">\n                    <jats:title>Funding<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>Health Technology Assessment<\/jats:italic>\n                      ; Vol. 30, No. 51. See the NIHR Funding and Awards website for further award information.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3310\/gjse4912","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T14:57:04Z","timestamp":1757689024000},"page":"1-89","update-policy":"https:\/\/doi.org\/10.3310\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review"],"prefix":"10.3310","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3516-277X","authenticated-orcid":true,"given":"Melina","family":"Vasileiou","sequence":"first","affiliation":[{"name":"BMJ Technology Assessment Group (BMJ-TAG), London, 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