Hong JC, Patel P, Eclov NCW, Stephens SJ, Mowery YM, Tenenbaum JD, et al. Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study. BMJ Health Care Inform. 2023;30(1):e100674.

Article  Google Scholar 

Marcu LG, Boyd C, Bezak E. Feeding the data monster: data science in head and neck cancer for personalized therapy. J Am Coll Radiol. 2019;16(12):1695–701.

Article  Google Scholar 

Weissler EH, Naumann T, Andersson T, Ranganath R, Elemento O, Luo Y, Freitag DF, Benoit J, Hughes MC, Khan F, Slater P, Shameer K, Roe M, Hutchison E, Kollins SH, Broedl U, Meng Z, Wong JL, Curtis L, Huang E, Ghassemi M. The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021;22:537.

Article  Google Scholar 

Lång K, Josefsson V, Larsson AM, Larsson S, Högberg C, Sartor H, Hofvind S, Andersson I, Rosso A. Artificial intelligence-supported screen reading versus standard double reading in the mammography screening with artificial intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023;24(8):936–44.

Article  Google Scholar 

Hernström V, Josefsson V, Sartor H, Schmidt D, Larsson AM, Hofvind S, et al. Screening performance and characteristics of breast cancer detected in the mammography screening with artificial intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. Lancet Digit Health. 2025;7(3):e175–83.

Article  Google Scholar 

Winkel DJ, Tong A, Lou B, Kamen A, Comaniciu D, Disselhorst JA, Rodríguez-Ruiz A, Huisman H, Szolar D, Shabunin I, Choi MH, Xing P, Penzkofer T, Grimm R, von Busch H, Boll DT. A novel deep learning based Computer-Aided diagnosis system improves the accuracy and efficiency of radiologists in reading biparametric magnetic resonance images of the prostate: results of a Multireader, multicase study. Invest Radiol. 2021;56(10):605–13.

Article  Google Scholar 

Hamm CA, Baumgärtner GL, Biessmann F, Beetz NL, Hartenstein A, Savic LJ, Froböse K, Dräger F, Schallenberg S, Rudolph M, Baur ADJ, Hamm B, Haas M, Hofbauer S, Cash H, Penzkofer T. Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology. 2023;307(4):e222276.

Article  Google Scholar 

Dembrower K, Crippa A, Colón E, Eklund M, Strand F, ScreenTrustCAD Trial Consortium. Artificial intelligence for breast cancer detection in screening mammography in sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit Health. 2023;5(10):e703–11.

Article  Google Scholar 

Hickman SE, Baxter GC, Gilbert FJ. Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. Br J Cancer. 2021;125:15–22.

Article  Google Scholar 

Salim M, Liu Y, Sorkhei M, Ntoula D, Foukakis T, Fredriksson I, et al. AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized screentrustmri trial. Nat Med. 2024;30(9):2623–30.

Article  Google Scholar 

Mann RM, Athanasiou A, Baltzer PAT, Camps-Herrero J, Clauser P, Fallenberg EM, et al. European society of breast imaging (EUSOBI). breast cancer screening in women with extremely dense breasts recommendations of the European society of breast imaging (EUSOBI). Eur Radiol. 2022;32(6):4036–45.

Article  Google Scholar 

Friedewald SM, Sieniek M, Jansen S, Mahvar F, Kohlberger T, Schacht D, et al. Triaging mammography with artificial intelligence: an implementation study. Breast Cancer Res Treat. 2025;211(1):1–10.

Article  Google Scholar 

Eisemann N, Bunk S, Mukama T, Baltus H, Elsner SA, Gomille T, et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nat Med. 2025;31(3):917–24.

Article  Google Scholar 

Chen PT, Wu T, Wang P, Chang D, Liu KL, Wu MS, et al. Pancreatic cancer detection on CT scans with deep learning: a nationwide population-based study. Radiology. 2023;306(1):172–82.

Article  Google Scholar 

Papp L, Spielvogel CP, Grubmüller B, Grahovac M, Krajnc D, Ecsedi B. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging. 2021;48(6):1795–805.

Article  Google Scholar 

Hartenstein A, Lübbe F, Baur ADJ, Rudolph MM, Furth C, Brenner W, et al. Prostate cancer nodal staging: using deep learning to predict 68Ga-PSMA-positivity from CT imaging alone. Sci Rep. 2020;10(1):3398.

Article  Google Scholar 

Ren C, Zhang F, Zhang J, Song S, Sun Y, Cheng J. Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer. Eur J Med Res. 2023;28(1):554.

Article  Google Scholar 

Yin HL, Jiang Y, Xu Z, Jia HH, Lin GW. Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions. J Cancer Res Clin Oncol. 2023;149(6):2575–84.

Article  Google Scholar 

Ma M, Liu R, Wen C, Xu W, Xu Z, Wang S, Wu J, Pan D, Zheng B, Qin G, Chen W. Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms. Eur Radiol. 2022;32(3):1652–62.

Article  Google Scholar 

Zhang W, Yin H, Huang Z, Zhao J, Zheng H, He D, et al. Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer. Cancer Med. 2021;10(12):4164–73.

Article  Google Scholar 

Zhang YF, Zhou C, Guo S, Wang C, Yang J, Yang ZJ, Wang R, Zhang X, Zhou FH. Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer. J Cancer Res Clin Oncol. 2024;150(2):78.

Article  Google Scholar 

Xie N, Fan X, Xie H, Lu J, Yu L, Liu H, et al. Preoperative extrapancreatic extension prediction in patients with pancreatic cancer using multiparameter MRI and machine learning-based radiomics model. Acad Radiol. 2023;30(7):1306–16.

Article  Google Scholar 

Li X, Guo Y, Huang S, Wang F, Dai C, Zhou J, et al. A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma. BMC Med Imaging. 2025;25(1):167.

Article  Google Scholar 

Liu H, Chen Y, Zhang Y, Wang L, Luo R, Wu H, Wu C, Zhang H, Tan W, Yin H, Wang D. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Eur Radiol. 2021;31(8):5902–12.

Article  Google Scholar 

Zhang H, Liao M, Guo Q, Chen J, Wang S, Liu S, et al. Predicting n2 lymph node metastasis in presurgical stage i-ii non-small cell lung cancer using multiview radiomics and deep learning method. Med Phys. 2023;50(4):2049–60.

Article  Google Scholar 

Eresen A, Li Y, Yang J, Shangguan J, Velichko Y, Yaghmai V, et al. Preoperative assessment of lymph node metastasis in colon cancer patients using machine learning: a pilot study. Cancer Imaging. 2020;20(1):30.

Article  Google Scholar 

Zheng H, Jian L, Li L, Liu W, Chen W. Prior clinico-radiological features informed multi-modal MR images convolution neural network: a novel deep learning framework for prediction of lymphovascular invasion in breast cancer. Cancer Med. 2024;13(3):e6932.

Article  Google Scholar 

Fan L, Li J, Zhang H, Yin H, Zhang R, Zhang J, Chen X. Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables. Abdom Radiol. 2022;47(4):1209–22.

Article  Google Scholar 

clinicaltrials.gov. (Accessed on 28/3/2025).

https://clinicaltrials.gov/study/NCT04605276 (Accessed on 21/5/2024).

https://pi-cai.grand-challenge.org/ (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT05481762 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT04022512 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT04688086 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT03708978 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT05048095 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT05640011 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT05594485 (Accessed on 21/5/2025).

https://clinicaltrials.gov/search?cond=NCT05963945 (Accessed on 21/5/2025).

Saha A, Bosma JS, Twilt JJ, van Ginneken B, Bjartell A, Padhani AR, et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol. 2024;25(7):879–87.

Article  Google Scholar 

Kvak D, Chromcová A, Hrubý R, Janů E, Biroš M, Pajdaković M, Kvaková K, Al-Antari MA, Polášková P, Strukov S. Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images. Diagnostics (Basel). 2023;13(6):1043. https://doi.org/10.3390/diagnostics13061043.

Article  Google Scholar 

Dratsch T, Chen X, Rezazade Mehrizi M, Kloeckner R, Mähringer-Kunz A, Püsken M, Baeßler B, Sauer S, Maintz D. Pinto Dos Santos D. Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology. 2023;307(4):e222176.

Article  Google Scholar 

Bernstein MH, Atalay MK, Dibble EH, Maxwell AWP, Karam AR, Agarwal S, Ward RC, Healey TT, Baird GL. Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography. Eur Radiol. 2023;33(11):8263–9.

Article  Google Scholar 

https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices (Accessed on 16/08/2025).

https://www.accessdata.fda.gov/cdrh_docs/pdf25/K251406.pdf (Accessed on 16/08/2025).

https://www.iomp.org/international-medical-physics-week-impw-2025/ (Accessed on 16/08/2025).