AI in imaging and therapy: innovations, ethics, and impact – introductory editorial

AI in imaging and therapy: innovations, ethics and impact: EditorialAI in imaging and therapy: innovations, ethics, and impact – introductory editorialIssam El Naqa and Karen DrukkerIssam El NaqaMoffitt Cancer Center, Tampa, Florida, USASearch for more papers by this author and Karen DrukkerUniversity of Chicago, Chicago, Illinois, USASearch for more papers by this authorPublished Online:25 Sep 2023https://doi.org/10.1259/bjr.20239004SectionsPDF/EPUBFull Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail About"AI in imaging and therapy: innovations, ethics, and impact – introductory editorial." The British Journal of Radiology, 96(1150), pp. REFERENCES1. Mello-Thoms C, Mello CAB. Clinical applications of artificial intelligence in radiology. Br J Radiol 2023; 96: 20221031. doi: https://doi.org/10.1259/bjr.20221031 Google Scholar2. Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, et al.. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. Br J Radiol 2023; 96: 20230211. doi: https://doi.org/10.1259/bjr.20230211 Google Scholar3. Drabiak K, Kyzer S, Nemov V, El Naqa I. AI and machine learning ethics, law, diversity, and global impact. Br J Radiol 2023; 96: 20220934. doi: https://doi.org/10.1259/bjr.20220934 Google Scholar4. Gichoya JW, Thomas K, Celi LA, Safdar N, Banerjee I, Banja JD, et al.. AI pitfalls and what not to do: mitigating bias in AI. Br J Radiol 2023; 96: 20230023. doi: https://doi.org/10.1259/bjr.20230023 Google Scholar5. Sahiner B, Chen W, Samala RK, Petrick N. Data drift in medical machine learning: implications and potential remedies. Br J Radiol 2023; 96: 20220878. doi: https://doi.org/10.1259/bjr.20220878 Google Scholar6. JinKW, LiQ, Xie Y, Xiao G. Artificial intelligence in mental healthcare: an overview and future perspectives. Br J Radiol 2023; 96: 20230213. doi: https://doi.org/10.1259/bjr.20230213 Google Scholar7. Cui S, Traverso A, Niraula D, Zou J, Luo Y, Owen D, et al.. Interpretable artificial intelligence in Radiology and radiation oncology. Br J Radiol 2023; 96: 20230142. doi: https://doi.org/10.1259/bjr.20230142 Google Scholar8. Armato SG, Drukker K, Hadjiiski L. AI in medical imaging grand challenges: translation from competition to research benefit and patient care. Br J Radiol 2023; 96: 20221152. doi: https://doi.org/10.1259/bjr.20221152 Google Scholar9. Rehman MHur, Hugo Lopez Pinaya W, Nachev P, Teo JT, Ourselin S, Cardoso MJ. Federated learning for medical imaging radiology: a review. Br J Radiol 2023; 96: 20220890. doi: https://doi.org/10.1259/bjr.20220890 Google Scholar10. Kelly BS, Judge C, Hoare S, Colleran G, Lawlor A, Killeen RP. How to apply evidence-based practice to the use of artificial intelligence in radiology (EBRAI) using the data algorithm training output (DATO) method. Br J Radiol 2023; 96: 20220215. doi: https://doi.org/10.1259/bjr.20220215 Google Scholar11. Brady SL. Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved. Br J Radiol 2023; 96: 20220915. doi: https://doi.org/10.1259/bjr.20220915 Medline, Google Scholar12. Reader AJ, Pan B. AI for PET image reconstruction. Br J Radiol 2023; 96: 20230292. doi: https://doi.org/10.1259/bjr.20230292 Google Scholar13. Yasaka K, Hatano S, Mizuki M, Okimoto N, Kubo T, Shibata E, et al.. Effects of deep learning on radiologists' and radiology residents' performance in identifying esophageal cancer on CT. Br J Radiol 2023; 96: 20220685. doi: https://doi.org/10.1259/bjr.20220685 Google Scholar Next article FiguresReferencesRelatedDetails Volume 96, Issue 1150October 2023 © 2023 The Authors. Published by the British Institute of Radiology History Published onlineSeptember 25,2023 Metrics Download PDF