The recent explosion of ‘big data’ has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it.
• Artificial intelligence (AI) research in medical imaging has a long history
• The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods.
• A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Artificial neural network
Least absolute shrinkage and selection operator
Patient/problem, intervention, comparison intervention and outcomes
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
Tang A, Tam R, Cadrin-Chênevert A et al Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group (2018) Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 69:120–135
Summers RM (2016) Progress in fully automated abdominal CT interpretation. AJR Am J Roentgenol 207:67–79
Matsuyama T (1989) Expert systems for image processing: knowledge-based composition of image analysis processes. Comput Vision Graph 48:22–49
Stansfield SA (1986) ANGY: a rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms. IEEE Trans Pattern Anal Mach Intell 2:188–199
Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22:483–492
Warfield SK, Zou KH, Wells WM. (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–21
Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal 26:1–18
Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24:205–219
Van Leemput K, Maes F, Vandermeulen D, Colchester A, Suetens P (2001) Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 20:677–688
Prastawa M, Bullitt E, Moon N, Van Leemput K, Gerig G (2003) Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad Radiol 10:1341–1348
Erus G, Zacharaki EI, Davatzikos C (2014) Individualized statistical learning from medical image databases: application to identification of brain lesions. Med Image Anal 18:542–554
Viergever MA, Maintz JBA, Klein S, Murphy K, Staring M, Pluim JPW (2016) A survey of medical image registration - under review. Med Image Anal 33:140–144
Kraus WL (2015) Editorial: would you like a hypothesis with those data? Omics and the age of discovery science. Mol Endocrinol 29:1531–1534
Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2:1636–1642
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
Matzner-Lober E, Suehs CM, Dohan A, Molinari N (2018) Thoughts on entering correlated imaging variables into a multivariable model: application to radiomics and texture analysis. Diagn Interv Imaging 99:269–270
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267–288
Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131
Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard Univ
Rosenblatt F (1957). The Perceptron—a perceiving and recognizing automaton. Report 85-460-1, Cornell Aeronautical Laboratory
Lawrence N (2016) Deep learning, Pachinko and James Watt: efficiency is the driver of uncertainty. http://inverseprobability.com/2016/03/04/deep-learning-and-uncertainty. Accessed 23 May 2018
Szegedy C, Zaremba W, Sutskever I et al (2013) Intriguing properties of neural networks. arXiv:1312.6199
Richardson WS, Wilson MC, Nishikawa J, Hayward RS (1995) The well-built clinical question: a key to evidence-based decisions. ACP J Club 123:A12–A13
Simmons JP, Nelson LD, Simonsohn U (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci 22:1359–1366
Ferrante E, Dokania PK, Marini R, Paragios N (2017) Deformable registration through learning of context-specific metric aggregation. Machine Learning in Medical Imaging Workshop. MLMI (MICCAI 2017), Sep 2017, Quebec City, Canada
The authors state that this work has not received any funding.
The scientific guarantor of this publication is Dr. Benoit Gallix.
Conflict of Interest
Professor Nikos Paragions declares a relationship with the following company: TheraPanacea, Paris, France.
The other co-authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and Biometry
No complex statistical methods were necessary for this paper.
Written informed consent was not required for this study because this is a review article, no study was performed.
Institutional review board approval was not required because this is a review article and no study was performed.
About this article
Cite this article
Savadjiev, P., Chong, J., Dohan, A. et al. Demystification of AI-driven medical image interpretation: past, present and future. Eur Radiol 29, 1616–1624 (2019). https://doi.org/10.1007/s00330-018-5674-x
- Diagnostic imaging
- Artificial intelligence (AI)
- Machine learning
- Computer-assisted image processing
- Computer-assisted image interpretation