Duraid Manea Bashara (1), Fathinul Fikri Ahmed Saad (2)
General Background: Radiology has evolved from analog image interpretation to data-intensive digital analysis, creating opportunities for artificial intelligence (AI) to support diagnostic and operational processes. Specific Background: AI, particularly deep learning and convolutional neural networks, is increasingly applied in lesion detection, image segmentation, image reconstruction, workflow triage, and radiomics. Knowledge Gap: Despite rapid adoption, a comprehensive synthesis of AI applications in radiology and the associated technical, ethical, and legal barriers remains necessary. Aims: This review examines current AI applications in medical imaging, their role in precision medicine, and the major challenges affecting clinical implementation. Results: AI demonstrated expert-level performance in detecting pulmonary nodules, breast cancer, and pancreatic lesions; automated segmentation improved quantitative assessment of tumors and neurodegenerative changes; deep learning reconstruction reduced radiation dose and shortened MRI acquisition time; triage systems prioritized urgent findings and reduced turnaround time; and radiomics and radiogenomics enabled non-invasive “virtual biopsy” and prognostic modeling. Novelty: This review integrates diagnostic, operational, and predictive roles of AI across the entire radiology workflow within the concept of augmented intelligence. Implications: AI is positioned as a collaborative tool that supports radiologists and advances precision medicine, while successful adoption depends on explainability, data generalizability, privacy protection, and clear regulatory frameworks.
Highlights:
• AI supports lesion detection, segmentation, and image reconstruction in medical imaging.• Intelligent triage and scheduling reduce turnaround time and improve radiology workflow.• Radiomics and radiogenomics enable non-invasive tumor characterization and prognosis prediction.
Keywords: Artificial Intelligence, Radiology, Deep Learning, Radiomics, Precision Medicine
Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2019;49(7):e37-e54.
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-514.
Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318-328.
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-157.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016;278(2):563-577.
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al. Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol. 2018;15(3 Pt B):504-508.
Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists are again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2(1):35.
Yasaka K, Abe O. Deep learning and artificial intelligence in radiology: Current applications and future directions. PLoS Med. 2018;15(11):e1002707.
Irshad H, Veillard A, Roux L, Racoceanu D. Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential. IEEE Rev Biomed Eng. 2013;7:97-114.
Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, et al. Development and validation of a deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 2019;290(1):218-228.
McKinney SM, Sieniek M, Godbole V, Godwin N, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
Hameed BS, Krishnan UM. Artificial intelligence-driven diagnosis of pancreatic cancer. Cancers. 2022;14(21):5382.
Fang D, Huang Y, Li S, Shi C, Bao J, Du D, et al. A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images. BMC Cancer. 2025;25(1):495.
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993-2024.
Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: State of the art and future directions. J Digit Imaging. 2017;30(4):449-459.
Yoo HJ. Deep convolution neural networks in computer vision: A review. IEIE Trans Smart Process Comput. 2015;4(1):35-43.
Lin CL, Wu KC. Development of revised ResNet-50 for diabetic retinopathy detection. BMC Bioinformatics. 2023;24(1):157.
Geyer LL, Schoepf UJ, Meinel FG, Nance JW Jr, Bastian ML, Canstein C, et al. State of the art: Iterative CT reconstruction techniques. Radiology. 2015;276(2):339-357.
Knoll F, Hammernik K, Zhang C, Moeller S, Pock T, Sodickson DK, et al. Deep-learning methods for parallel magnetic resonance imaging reconstruction: A review of the current state of the art. IEEE Signal Process Mag. 2020;37(1):128-140.
Uzelaltinbulat S, Ugur B. Lung tumor segmentation algorithm. Procedia Comput Sci. 2017;120:140-147.
Heit JJ, Iv M, Wintermark M. Continuous high-performance artificial intelligence: The future of neuroradiology. AJNR Am J Neuroradiol. 2019;40(10):1618-1620.
Branstetter BF, et al. The radiology report: Are we getting better? J Am Coll Radiol. 2017;14(10):1335-1341.
McDonald RJ, et al. The effects of changing radiology report turnaround time. AJR Am J Roentgenol. 2015;204(6):1220-1227.
Merali Z, et al. AI for reducing no-show rates in radiology. J Am Coll Radiol. 2021;18(9):1280-1289.
Harvey H, et al. AI in radiology: The opportunity for efficiency. Clin Radiol. 2019;74(5):379-384.
Chong J, et al. Utilization of AI in radiology operations. Radiol Artif Intell. 2021;3(5):e200193.
Sodickson DK, et al. Modernizing radiology workflows through AI. Nat Mach Intell. 2019;1(5):215-224.
Lakhani P, et al. Machine learning in radiology. Radiographics. 2018;38(6):1662-1679.
Gichoya JW, et al. AI in medical imaging. J Digit Imaging. 2020;33(1):1-3.
Suess C, et al. AI for quality control. J Digit Imaging. 2020;33(2):345-352.
Samala RK, et al. Deep learning in medical imaging. Phys Med Biol. 2018;63(24):24TR01.
Annarumma S, et al. Automated triaging of chest radiographs. Radiology. 2019;291(1):196-202.
Chilamkurthy S, et al. Deep learning algorithms for the detection of critical findings in head CT scans. Lancet. 2018;392(10162):2388-2396.
Titano JJ, et al. Automated deep-learning triage of head CT. Nat Med. 2018;24(9):1337-1341.
Lambin P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-446.
Aerts HJ, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
Kumar V, et al. Radiomics: The process and the challenges. Magn Reson Imaging. 2012;30(9):1234-1248.
Limkin EJ, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28(6):1191-1206.
Kuo MD, Jamshidi N. Behind the numbers: Decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology. 2014;270(2):320-325.
Kickingereder P, et al. Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology. 2016;280(3):880-889.
Gevaert O, et al. Non-small cell lung cancer: Identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results. Cancer Res. 2012;72(10):2720-2728.
Coroller TP, et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol. 2016;119(3):480-486.
Rizzo S, et al. Radiomics: The facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.
Trebeschi S, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol. 2019;30(6):998-1004.
Sun R, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: An imaging biomarker, retrospective multicohort study. Lancet Digit Health. 2019;1(5):e232-e239.
Braman NM, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer. Breast Cancer Res. 2017;19(1):57.
Nie K, et al. Rectal cancer: Assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res. 2016;22(21):5256-5264.
Jaremko JL, et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019;70(2):107-118.
Castelvecchi D. Can we open the black box of AI? Nature. 2016;538(7623):20-23.
Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI): Toward medical XAI. IEEE Trans Neural Netw Learn Syst. 2021;32(11):4793-4813.
Ghassemi M, et al. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745-e750.
Zech JR, et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med. 2018;15(11):e1002683.
Kelly CJ, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195.
Seyyed-Kalantari L, et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021;27(12):2176-2182.
Larrazabal AJ, et al. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci U S A. 2020;117(23):12592-12594.
Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. 2019;322(18):1765-1766.
Geis JR, et al. Ethics of artificial intelligence in radiology: Summary of the Joint European and North American Multisociety Statement. J Am Coll Radiol. 2019;16(11):1516-1521.
Parasuraman R, Manzey DH. Complacency and bias in human use of automation: An attentional integration. Hum Factors. 2010;52(3):381-410.
Lyell D, et al. Automation bias in mammography: The effect of artificial intelligence decision support on reader performance. J Am Med Inform Assoc. 2021;28(11):2379-2388.
Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43.
Kaissis GA, et al. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell. 2020;2(6):305-311.
Pesapane F, et al. Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights Imaging. 2018;9(5):745-753.
Langlotz CP. Will artificial intelligence replace radiologists? Radiol Artif Intell. 2019;1(3):e190058.
Curtis C, et al. The future of radiology in the age of artificial intelligence. J Am Coll Radiol. 2021;18(3):450-455.
Arbabshirani MR, et al. Advanced machine learning in action: Identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med. 2018;1:9.
European Society of Radiology (ESR). What the radiologist should know about artificial intelligence: An ESR white paper. Insights Imaging. 2019;10(1):44.
Giger ML. Machine learning in medical imaging. J Am Coll Radiol. 2018;15(3 Pt B):512-520.