
1) The Royal College of Radiologists. Clinical Radiology Census Report 2022. London: The Royal College of Radiologists, 2023. Available at: https://www.rcr.ac.uk/news-policy/policy-reports-initiatives/clinical-radiology-census-reports/ (Accessed 07/01/2024)
2) Stogiannos N, Malik R, Kumar A, Barnes A, Pogose M, Harvey H, McEntee MF, Malamateniou C. Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK. Br J Radiol 2023 Dec; 96(1152):20221157. doi: 10.1259/bjr.20221157
3) Daye D, Wiggins WF, Lungren MP, Alkasab T, Kottler N, Allen B, Roth CJ, Bizzo BC, Durniak K, Brink JA, Larson DB, Dreyer KJ, Langlotz CP. Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? Radiology 2022; 305(1): E62. doi: 10.1148/radiol.229021
4) Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022 Oct 31;22(1):187. doi: 10.1186/s12880-022-00918-y
5) The Royal College of Radiologists. Integrating artificial intelligence with the radiology reporting workflows (RIS and PACS), London: The Royal College of Radiologists, 2021. Available at: https://www.rcr.ac.uk/our-services/all-our-publications/clinical-radiology-publications/integrating-artificial-intelligence-with-the-radiology-reporting-workflows-ris-and-pacs/ (Accessed 07/01/2024)
6) McCague C, MacKay K, Welsh C, Constantinou A, Jena R, Crispin-Ortuzar M; Imaging AI evaluation consensus group. Position statement on clinical evaluation of imaging AI. Lancet Digit Health 2023; 5(7): e400-e402. doi: 10.1016/S2589-7500(23)00090-0
7) Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 2021; 11: e048008. doi: 10.1136/bmjopen-2020-048008
8) Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P; DECIDE-AI expert group. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 2022; 28(5):924-933. doi: 10.1038/s41591-022-01772-9
9) Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020; 26(9):1364-1374. doi: 10.1038/s41591-020-1034-x
10) The Royal College of Radiologists. Overcoming Barriers to AI Implementation in Imaging: Outcome of an RCR Expert Stakeholder Day 2023. Available at: https://www.rcr.ac.uk/our-services/artificial-intelligence-ai/overcoming-barriers-to-ai-implementation-in-imaging/ (Accessed 06/01/2024)
11) van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 2021; 31(6): 3797-3804. doi: 10.1007/s00330-021-07892-z
12) Pemberton, H.G., Zaki, L.A.M., Goodkin, O. et al. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis—a systematic review. Neuroradiology 2021; 63: 1773–1789. https://doi.org/10.1007/s00234-021-02746-3
13) Frood R, Willaime J, Miles B, Chambers G, Al-Chalabi H, Ali T, Hougham N, Brooks N, Petrides G, Naylor M, Ward D, Sulin T, Chaytor R, Strouhal P, Patel C, Scarsbrook A. Comparative effectiveness of standard versus AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging -multi-institutional reader study evaluation. Front. Nucl. Med. Sec. Radiomics and Artificial Intelligence Volume 3 - 2023 | doi 10.3389/fnume.2023.1327186
14) Morton, W. AI increases PET/CT reporting efficiency in lymphoma staging. AuntMinnie 2022. Available at: https://www.auntminnie.com/clinical-news/molecular-imaging/article/15632438/ai-increases-pet-ct-reporting-efficiency-in-lymphoma-staging (Accessed 06/01/2024)