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Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/80342, first published .
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Implementing an Artificial Intelligence Decision Support System in Radiology: Prospective Qualitative Evaluation Study Using the Nonadoption Abandonment Scale-Up, Spread, and Sustainability (NASSS) Framework

Implementing an Artificial Intelligence Decision Support System in Radiology: Prospective Qualitative Evaluation Study Using the Nonadoption Abandonment Scale-Up, Spread, and Sustainability (NASSS) Framework

Journals

  1. Obilaja O, Okeke A, Nwosu-Ijiomah C, Ayeyemi B, Mensah A. Beyond Diagnostic Accuracy: Evaluating the Real-World Clinical Impact of AI-Enabled Radiology in Oncology and Nuclear Medicine. Oncology, Nuclear Medicine and Transplantology 2026;2(1):onmt016 View
  2. Chendeb El Rai M, Beya Far A, Darweesh M, Dhou S, Aburaed N, El Rai S, ElKhazendar M, Ellahham S. Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review. Diagnostics 2026;16(10):1485 View
  3. Chupetlovska K, Georganta E, Dignum W, Yadav S, Nguyen-Kim T, Beets-Tan R, Trebeschi S. From resistance to reliance: A human-centered analysis of the spectrum of radiologists' trust in AI. European Journal of Radiology Open 2026;17:100780 View
  4. Packer J, Dean G, Storey M, Malamateniou C, Shelmerdine S. Responsible Artificial Intelligence Off-Boarding in Radiology: Staff Perspectives on Decommissioning and a Proposed Withdrawal Framework. Journal of the American College of Radiology 2026 View
  5. Koopmans L, Vega Lara F, Roest C, Turkbey B, Yakar D, Kwee T. Autonomous AI in prostate cancer: the road ahead towards clinical implementation. Abdominal Radiology 2026 View
  6. Tariq A, Naicker S, Iqbal U, McPhail S. Why integration, not innovation, is the real-world challenge facing digital health. BMJ Health & Care Informatics 2026;33(1):e102218 View