How Does Artificial Intelligence in Radiology Improve Efficiency and Health Outcomes?

Overview

Journal Pediatr Radiol

Specialty Pediatrics

Date 2021 Jun 12

PMID 34117522

Citations 54

Authors

Kicky G van Leeuwen

Maarten de Rooij

Steven Schalekamp

Bram van Ginneken

Matthieu J C M Rutten

Affiliations

Soon will be listed here.

Abstract

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.

Citing Articles

Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.

Shetty S, Talaat W, Al-Rawi N, Al Kawas S, Sadek M, Elayyan M Sci Rep. 2025; 15(1):8324.

PMID: 40064998 PMC: 11894202. DOI: 10.1038/s41598-025-93250-8.

Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism.

Mohanarajan M, Salunke P, Arif A, Iglesias Gonzalez P, Ospina D, Benavides D Cureus. 2025; 17(1):e78217.

PMID: 40026993 PMC: 11872007. DOI: 10.7759/cureus.78217.

The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.

Chowdhury M, Rizk R, Chiu C, Zhang J, Scholl J, Bosch T Biomedicines. 2025; 13(2).

PMID: 40002840 PMC: 11852486. DOI: 10.3390/biomedicines13020427.

AI for image quality and patient safety in CT and MRI.

Melazzini L, Bortolotto C, Brizzi L, Achilli M, Basla N, DOnorio De Meo A Eur Radiol Exp. 2025; 9(1):28.

PMID: 39987533 PMC: 11847764. DOI: 10.1186/s41747-025-00562-5.

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

Kim S, Schramm S, Riedel E, Schmitzer L, Rosenkranz E, Kertels O Radiol Med. 2025; .

PMID: 39939458 DOI: 10.1007/s11547-025-01964-6.

References

Kim J, Shim W, Yoon H, Hong S, Lee J, Cho Y . Computerized Bone Age Estimation Using Deep Learning Based Program: Evaluation of the Accuracy and Efficiency. AJR Am J Roentgenol. 2017; 209(6):1374-1380. DOI: 10.2214/AJR.17.18224. View

Jans L, Chen M, Elewaut D, Van den Bosch F, Carron P, Jacques P . MRI-based Synthetic CT in the Detection of Structural Lesions in Patients with Suspected Sacroiliitis: Comparison with MRI. Radiology. 2020; 298(2):343-349. DOI: 10.1148/radiol.2020201537. View

Chong L, Tsai K, Lee L, Foo S, Chang P . Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows. AJR Am J Roentgenol. 2020; 215(5):1155-1162. DOI: 10.2214/AJR.19.22594. View

Alshamrani K, Offiah A . Applicability of two commonly used bone age assessment methods to twenty-first century UK children. Eur Radiol. 2019; 30(1):504-513. PMC: 6890594. DOI: 10.1007/s00330-019-06300-x. View

Bakker M, de Lange S, Pijnappel R, Mann R, Peeters P, Monninkhof E . Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. N Engl J Med. 2019; 381(22):2091-2102. DOI: 10.1056/NEJMoa1903986. View

Rodriguez-Ruiz A, Krupinski E, Mordang J, Schilling K, Heywang-Kobrunner S, Sechopoulos I . Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2018; 290(2):305-314. DOI: 10.1148/radiol.2018181371. View

Schalekamp S, Karssemeijer N, Cats A, de Hoop B, Geurts B, Berger-Hartog O . The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study. J Thorac Imaging. 2016; 31(2):119-25. DOI: 10.1097/RTI.0000000000000195. View

Hassan A . New Technology Add-On Payment (NTAP) for Viz LVO: a win for stroke care. J Neurointerv Surg. 2020; 13(5):406-408. PMC: 8053346. DOI: 10.1136/neurintsurg-2020-016897. View

Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P . Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019; 111(9):916-922. PMC: 6748773. DOI: 10.1093/jnci/djy222. View

10.

Willemink M, Noel P . The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol. 2018; 29(5):2185-2195. PMC: 6443602. DOI: 10.1007/s00330-018-5810-7. View

11.

Porter M . What is value in health care?. N Engl J Med. 2010; 363(26):2477-81. DOI: 10.1056/NEJMp1011024. View

12.

Grunwald I, Ragoschke-Schumm A, Kettner M, Schwindling L, Roumia S, Helwig S . First Automated Stroke Imaging Evaluation via Electronic Alberta Stroke Program Early CT Score in a Mobile Stroke Unit. Cerebrovasc Dis. 2016; 42(5-6):332-338. DOI: 10.1159/000446861. View

13.

Gerke S, Babic B, Evgeniou T, Cohen I . The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit Med. 2020; 3:53. PMC: 7138819. DOI: 10.1038/s41746-020-0262-2. View

14.

Qin Z, Sander M, Rai B, Titahong C, Sudrungrot S, Laah S . Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019; 9(1):15000. PMC: 6802077. DOI: 10.1038/s41598-019-51503-3. View

15.

Fenton J, Taplin S, Carney P, Abraham L, Sickles E, DOrsi C . Influence of computer-aided detection on performance of screening mammography. N Engl J Med. 2007; 356(14):1399-409. PMC: 3182841. DOI: 10.1056/NEJMoa066099. View

16.

Hassan A, Ringheanu V, Rabah R, Preston L, Tekle W, Qureshi A . Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model. Interv Neuroradiol. 2020; 26(5):615-622. PMC: 7645178. DOI: 10.1177/1591019920953055. View

17.

Nehrer S, Ljuhar R, Steindl P, Simon R, Maurer D, Ljuhar D . Automated Knee Osteoarthritis Assessment Increases Physicians' Agreement Rate and Accuracy: Data from the Osteoarthritis Initiative. Cartilage. 2019; 13(1_suppl):957S-965S. PMC: 8808932. DOI: 10.1177/1947603519888793. View

18.

Wilensky G . Robotic Surgery: An Example of When Newer Is Not Always Better but Clearly More Expensive. Milbank Q. 2016; 94(1):43-6. PMC: 4941968. DOI: 10.1111/1468-0009.12178. View

19.

Dagan N, Elnekave E, Barda N, Bregman-Amitai O, Bar A, Orlovsky M . Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization. Nat Med. 2020; 26(1):77-82. DOI: 10.1038/s41591-019-0720-z. View

20.

ONeill T, Xi Y, Stehel E, Browning T, Ng Y, Baker C . Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage. Radiol Artif Intell. 2021; 3(2):e200024. PMC: 8043365. DOI: 10.1148/ryai.2020200024. View