Artificial Intelligence-Empowered Radiology—Current Status and Critical Review

Rafał Obuchowicz; Julia Lasek; Marek Wodziński; Adam Piorkowski; Michał Strzelecki; Karolina Nurzynska

doi:10.3390/diagnostics15030282

journal article Open Access Jan 24, 2025

Artificial Intelligence-Empowered Radiology—Current Status and Critical Review

Rafał Obuchowicz

Diagnostics Vol. 15 No. 3 pp. 282 · MDPI AG

View at Publisher Save 10.3390/diagnostics15030282

Abstract

Humanity stands at a pivotal moment of technological revolution, with artificial intelligence (AI) reshaping fields traditionally reliant on human cognitive abilities. This transition, driven by advancements in artificial neural networks, has transformed data processing and evaluation, creating opportunities for addressing complex and time-consuming tasks with AI solutions. Convolutional networks (CNNs) and the adoption of GPU technology have already revolutionized image recognition by enhancing computational efficiency and accuracy. In radiology, AI applications are particularly valuable for tasks involving pattern detection and classification; for example, AI tools have enhanced diagnostic accuracy and efficiency in detecting abnormalities across imaging modalities through automated feature extraction. Our analysis reveals that neuroimaging and chest imaging, as well as CT and MRI modalities, are the primary focus areas for AI products, reflecting their high clinical demand and complexity. AI tools are also used to target high-prevalence diseases, such as lung cancer, stroke, and breast cancer, underscoring AI’s alignment with impactful diagnostic needs. The regulatory landscape is a critical factor in AI product development, with the majority of products certified under the Medical Device Directive (MDD) and Medical Device Regulation (MDR) in Class IIa or Class I categories, indicating compliance with moderate-risk standards. A rapid increase in AI product development from 2017 to 2020, peaking in 2020 and followed by recent stabilization and saturation, was identified. In this work, the authors review the advancements in AI-based imaging applications, underscoring AI’s transformative potential for enhanced diagnostic support and focusing on the critical role of CNNs, regulatory challenges, and potential threats to human labor in the field of diagnostic imaging.

Topics

No keywords indexed for this article. Browse by subject →

References

178

[1]

Artificial intelligence in healthcare: transforming the practice of medicine

Junaid Bajwa, Usman Munir, Aditya Nori et al.

Future Healthcare Journal 2021 10.7861/fhj.2021-0095

[2]

Morris "Reinventing Radiology: Big Data and the Future of Medical Imaging" J. Thorac. Imaging (2018) 10.1097/rti.0000000000000311

[3]

Qureshi "The Radiological Renaissance: Transformative Advances in Emergency Care" Cosm. J. Biol. (2024)

[4]

"Ai Tools in Decision Making Support Systems: A Review" Int. J. Artif. Intell. Tools (2012) 10.1142/s0218213012400052

[5]

Soori, M., Jough, F.K.G., Dastres, R., and Arezoo, B. (2024). AI-Based Decision Support Systems in Industry 4.0, A Review. J. Econ. Technol. 10.1016/j.ject.2024.08.005

[6]

Pierce, R.L., Van Biesen, W., Van Cauwenberge, D., Decruyenaere, J., and Sterckx, S. (2022). Explainability in Medicine in an Era of AI-Based Clinical Decision Support Systems. Front. Genet., 13. 10.3389/fgene.2022.903600

[7]

Raparthi "AI-Driven Decision Support Systems for Precision Medicine: Examining the Development and Implementation of AI-Driven Decision Support Systems in Precision Medicine" J. Artif. Intell. Res. (2021)

[8]

Introduction to artificial intelligence in medicine

Yoav Mintz, Ronit Brodie

Minimally Invasive Therapy & Allied Technologi... 2019 10.1080/13645706.2019.1575882

[9]

Lai, V., Chen, C., Liao, Q.V., Smith-Renner, A., and Tan, C. (2021). Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies. arXiv.

[10]

De Vreede, T., Raghavan, M., and De Vreede, G.-J. (2021, January 5–8). Design Foundations for AI Assisted Decision Making: A Self Determination Theory Approach. Proceedings of the 54th Hawaii International Conference on System Sciences, Kauai, HI, USA. 10.24251/hicss.2021.019

[11]

Yang, X., and Aurisicchio, M. (2021, January 6). Designing Conversational Agents: A Self-Determination Theory Approach. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, ACM, Yokohama, Japan. 10.1145/3411764.3445445

[12]

Wang "Where Does AlphaGo Go: From Church-Turing Thesis to AlphaGo Thesis and Beyond" IEEE/CAA J. Autom. Sin. (2016) 10.1109/jas.2016.7471613

[13]

Artificial intelligence in medicine

Pavel Hamet, Johanne Tremblay

Metabolism 2017 10.1016/j.metabol.2017.01.011

[14]

Amisha "Overview of Artificial Intelligence in Medicine" J. Fam. Med. Prim. Care (2019) 10.4103/jfmpc.jfmpc_440_19

[15]

Homan "Applications of Artificial Intelligence (AI) in Diagnostic Radiology: A Technography Study" Eur. Radiol. (2021) 10.1007/s00330-020-07230-9

[16]

Artificial intelligence in radiology

Ahmed Hosny, Chintan Parmar, John Quackenbush et al.

Nature Reviews Cancer 2018 10.1038/s41568-018-0016-5

[17]

Tariq "Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence" J. Am. Coll. Radiol. (2020) 10.1016/j.jacr.2020.08.018

[18]

Kapoor "Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools" J. Am. Coll. Radiol. (2020) 10.1016/j.jacr.2020.08.016

[19]

Yamashita "Convolutional Neural Networks: An Overview and Application in Radiology" Insights Imaging (2018) 10.1007/s13244-018-0639-9

[20]

Schalekamp "Artificial Intelligence in Radiology: 100 Commercially Available Products and Their Scientific Evidence" Eur. Radiol. (2021) 10.1007/s00330-021-07892-z

[21]

Wichmann "Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation" Invest. Radiol. (2020) 10.1097/rli.0000000000000673

[22]

Pesapane, F., and Summers, P. (2024). Ethics and Regulations for AI in Radiology. Artificial Intelligence for Medicine, Elsevier. 10.1016/b978-0-443-13671-9.00001-6

[23]

Maleki "Machine Learning Algorithm Validation" Neuroimaging Clin. N. Am. (2020) 10.1016/j.nic.2020.08.004

[24]

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Communications of the ACM 2017 10.1145/3065386

[25]

Sutskever, I. (2013). Training Recurrent Neural Networks. [Ph.D. Thesis, University of Toronto].

[26]

Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

Zifeng Wu, Chunhua Shen, Anton van den Hengel

Pattern Recognition 2019 10.1016/j.patcog.2019.01.006

[27]

ImageNet: A large-scale hierarchical image database

Jia Deng, Wei Dong, Richard Socher et al.

2009 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2009.5206848

[28]

Witt, S. (2024, December 01). How Jensen Huang’s Nvidia Is Powering the A.I. Revolution. The New Yorker, Available online: https://www.newyorker.com/magazine/2023/12/04/how-jensen-huangs-nvidia-is-powering-the-ai-revolution.

[29]

Goriparthi "Deep Learning Architectures for Real-Time Image Recognition: Innovations and Applications" J. Data Sci. Intell. Syst. (2024)

[30]

A survey of the recent architectures of deep convolutional neural networks

Asifullah Khan, Anabia Sohail, Umme Zahoora et al.

Artificial Intelligence Review 2020 10.1007/s10462-020-09825-6

[31]

Cheng "Recent Advances in Efficient Computation of Deep Convolutional Neural Networks" Front. Inf. Technol. Electron. Eng. (2018) 10.1631/fitee.1700789

[32]

Ali, I., Hart, G.R., Gunabushanam, G., Liang, Y., Muhammad, W., Nartowt, B., Kane, M., Ma, X., and Deng, J. (2018). Lung Nodule Detection via Deep Reinforcement Learning. Front. Oncol., 8. 10.3389/fonc.2018.00108

[33]

Obuchowicz, R., Strzelecki, M., and Piórkowski, A. (2024). Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review. Cancers, 16. 10.3390/books978-3-7258-1260-8

[34]

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren et al.

2016 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2016.90

[35]

Schulz, S., Woerl, A.-C., Jungmann, F., Glasner, C., Stenzel, P., Strobl, S., Fernandez, A., Wagner, D.-C., Haferkamp, A., and Mildenberger, P. (2021). Multimodal Deep Learning for Prognosis Prediction in Renal Cancer. Front. Oncol., 11. 10.3389/fonc.2021.788740

[36]

Yi "Can AI Outperform a Junior Resident? Comparison of Deep Neural Network to First-Year Radiology Residents for Identification of Pneumothorax" Emerg. Radiol. (2020) 10.1007/s10140-020-01767-4

[37]

Chamberlin, J.H., Aquino, G., Nance, S., Wortham, A., Leaphart, N., Paladugu, N., Brady, S., Baird, H., Fiegel, M., and Fitzpatrick, L. (2022). Automated Diagnosis and Prognosis of COVID-19 Pneumonia from Initial ER Chest X-Rays Using Deep Learning. BMC Infect. Dis., 22. 10.1186/s12879-022-07617-7

[38]

Abrahamsen, B.S., Knudtsen, I.S., Eikenes, L., Bathen, T.F., and Elschot, M. (2023). Pelvic PET/MR Attenuation Correction in the Image Space Using Deep Learning. Front. Oncol., 13. 10.3389/fonc.2023.1220009

[39]

Wang, C., Shao, J., Xu, X., Yi, L., Wang, G., Bai, C., Guo, J., He, Y., Zhang, L., and Yi, Z. (2022). DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules. Front. Oncol., 12. 10.3389/fonc.2022.683792

[40]

Albiol "A Comparison of COVID-19 Early Detection between Convolutional Neural Networks and Radiologists" Insights Imaging (2022) 10.1186/s13244-022-01250-3

[41]

Densely Connected Convolutional Networks

Gao Huang, Zhuang Liu, Laurens Van Der Maaten et al.

2017 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2017.243

[42]

Rethinking the Inception Architecture for Computer Vision

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al.

2016 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2016.308

[43]

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v31i1.11231

[44]

Fink "A Deep Learning Approach for Projection and Body-Side Classification in Musculoskeletal Radiographs" Eur. Radiol. Exp. (2024) 10.1186/s41747-023-00417-x

[45]

Arbabshirani "Advanced Machine Learning in Action: Identification of Intracranial Hemorrhage on Computed Tomography Scans of the Head with Clinical Workflow Integration" NPJ Digit. Med. (2018) 10.1038/s41746-017-0015-z

[46]

Nguyen "Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network" J. Digit. Imaging (2022) 10.1007/s10278-021-00533-3

[47]

Solak "Adrenal Lesion Classification with Abdomen Caps and the Effect of ROI Size" Phys. Eng. Sci. Med. (2023) 10.1007/s13246-023-01259-y

[48]

Gasulla, Ó., Ledesma-Carbayo, M.J., Borrell, L.N., Fortuny-Profitós, J., Mazaira-Font, F.A., Barbero Allende, J.M., Alonso-Menchén, D., García-Bennett, J., Del Río-Carrrero, B., and Jofré-Grimaldo, H. (2023). Enhancing Physicians’ Radiology Diagnostics of COVID-19’s Effects on Lung Health by Leveraging Artificial Intelligence. Front. Bioeng. Biotechnol., 11. 10.3389/fbioe.2023.1010679

[49]

Santhanam "Artificial Intelligence and Body Composition" Diabetes Metab. Syndr.: Clin. Res. Rev. (2023) 10.1016/j.dsx.2023.102732

[50]

Willemink "Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks" Radiol. Artif. Intell. (2022) 10.1148/ryai.210284

Showing 50 of 178 references

Cited By

61

Hybrid AI systems in breast cancer histopathology classification: a systematic review and meta-analysis

Mustafa Adil Albuhadeed, Aqilah Baseri Huddin · 2026

PeerJ Computer Science

Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions

Muhammad Liaquat Raza, Syed Tawassul Hassan · 2025

Frontiers in Neuroinformatics

Metrics

61

Citations

178

References

Details

Published: Jan 24, 2025
Vol/Issue: 15(3)
Pages: 282
License: View

Authors

R

Rafał Obuchowicz

Department of Diagnostic Imaging, Jagiellonian University Medical College, 30-663 Krakow, Poland

J

Julia Lasek

Faculty of Geology, Geophysics and Environmental Protection, AGH University of Krakow, 30-059 Krakow, Poland

M

Marek Wodziński

Department of Measurement and Electronics, AGH University of Krakow, 30-059 Krakow, Poland

A

Adam Piorkowski

Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, 30-059 Krakow, Poland

M

Michał Strzelecki

Institute of Electronics, Lodz University of Technology, 93-590 Lodz, Poland

K

Karolina Nurzynska

Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland

Cite This Article

Rafał Obuchowicz, Julia Lasek, Marek Wodziński, et al. (2025). Artificial Intelligence-Empowered Radiology—Current Status and Critical Review. Diagnostics, 15(3), 282. https://doi.org/10.3390/diagnostics15030282

Artificial Intelligence-Empowered Radiology—Current Status and Critical Review

You May Also Like