Artificial intelligence in radiology

Hosny, Ahmed; Parmar, Chintan; Quackenbush, John; Schwartz, Lawrence H.; Aerts, Hugo J. W. L.

doi:10.1038/s41568-018-0016-5

Cited by 3,803AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Artificial intelligence in radiology

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Hosny, Ahmed, Parmar, Chintan, Quackenbush, John, Schwartz, Lawrence H., Aerts, Hugo J. W. L.·2018·Nature Reviews Cancer

DOI 10.1038/s41568-018-0016-5PMC PMC6268174

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Abstract

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Recent advances in AI-powered tools have shown similar or better sensitivity for detecting minuscule deviations from normal anatomy compared to a human assessor.
“For instance, recent advances in AI-powered tools have shown similar or better sensitivity for detecting minuscule deviations from normal anatomy, compared to a human assessor (20, 38–40).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [40]
The ‘AI in medical imaging’ section (page not provided) reports that deep learning matched radiologists on some detection tasks and had higher sensitivity for classifying lymph-node metastases in PET-CT. The paper does not establish comparable or better sensitivity specifically for detecting minuscule deviations from normal anatomy; its ‘Future perspectives’ section attributes discrimination of minute tissue-density differences to imaging hardware. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Deep learning can successfully be used for identifying and segmenting objects within the 3-dimensional image space.
“Especially in medical imaging there is a large potential as deep learning can successfully be used for identifying and segmenting objects within the 3-dimensional (3D) image space13–16.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [13]
Many AI applications in health care have demonstrated performance that matches or even surpasses that of human physicians in medical diagnosis, execution of treatment and surgery.
“Many AI applications in health care have demonstrated performance that matches or even surpasses that of human physicians in medical diagnosis, execution of treatment and surgery.1–3”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [3]
AI systems make errors in reading, classifying, and reconstructing radiological images, but the error rate is decreasing, and AI systems will soon outperform humans in terms of speed and accuracy of image reading.
“AI systems also make errors in reading, classifying, and reconstructing radiological images, but the error rate is decreasing, and AI systems will soon outperform humans in terms of speed and accuracy of image reading [12, 17, 103, 228].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [103]
An average radiologist now needs to interpret one image every 3-4 s to keep up with clinical workloads.
“Studies have shown that an average radiologist now needs to interpret one image every 3–4 s to keep up with clinical workloads3–5.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [4]

AI can assist radiologists in analyzing medical images by quickly identifying anomalies in x-rays or MRIs, flagging potential issues for closer inspection.
“Consider radiology, where AI assists radiologists in analyzing vast volumes of medical images where algorithms can quickly identify anomalies in x-rays or MRIs, flagging potential issues and requiring closer inspection [116].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [116]
In chatbot counselling, the dependence on mental healthcare chatbots does not have the reciprocity and transparency of treatment that should occur in the partnership between the mental healthcare patients and the healthcare professional.
“In chatbot counselling, another use of artificial intelligence is that the dependence on mental healthcare chatbots does not have the reciprocity and transparency of treatment that should occur in the partnership between the mental healthcare patients and the healthcare professional”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Hosny et al. 2018
Computerized tomography (CT) and MRI scanning are researching AI in radiology to identify and treat illness in patients.
“Computerized tomography (CT) and MRI scanning are researching AI in radiology to identify and treat illness in patients”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Hosny et al. 2018
While the possibility of AI replacing radiologists was extensively mentioned in recent radiology publications, none of the interviewees in this research identified with this threat.
“While the possibility of AI replacing radiologists, and thereby threatening their professional identity, was extensively mentioned in many recent radiology publications [5, 9, 16, 44], none of the interviewees in this research identified with this threat.”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [16]
Literature has mainly blamed the failure of early CAD systems on low technical performance, while other potential barriers such as organizational or social aspects have been largely ignored.
“Literature has mainly blamed this on low technical performance of these early applications [5, 14–16], while other potential barriers to successful implementation, such as organizational or social aspects, have been largely ignored [17].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [16]
AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.
“For instance, AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.30,31”
From Artificial intelligence: opportunities and implications for the health workforce (2020) · cites this paper as [30]
Deep learning algorithms have been widely applied in the field of image diagnosis and prediction owing to their advantages of being fast, accurate, and reproducible.
“Deep learning algorithms have been widely applied in the field of image diagnosis and prediction owing to their advantages of being fast, accurate, and reproducible [15].”
From Automated prediction of early spontaneous miscarriage based on the analyzing ultrasonographic gestational sac imaging by the convolutional neural network: a case-control and cohort study (2022) · cites this paper as [15]

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Authors: Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L.
Affiliations: Harvard University (US)
Dana-Farber Cancer Institute (US)
NewYork–Presbyterian Hospital (US)
Brigham and Women's Hospital (US)
Corresponding author: Aerts, Hugo J. W. L.
ORCID: Hosny, Ahmed
Parmar, Chintan
Quackenbush, John
Aerts, Hugo J. W. L.
Venue: Nature Reviews Cancer
Publisher: Springer Science and Business Media LLC
Published: 2018-05-17
Volume: 18
Issue: 8
Pages: 500-510
ISSN: 1474-175X (ISSN-L)
1474-1768
DOI: 10.1038/s41568-018-0016-5
PMC: PMC6268174
OpenAlex: W2803760365
Cited by: 3,803
References: 117
Citations by year: 2026: 454 · 2025: 677 · 2024: 693 · 2023: 602 · 2022: 483 · 2021: 449 · 2020: 303 · 2019: 127 · 2018: 15
FWCI: 26.15
Type: journal article
Language: en
Open access: green
europepmc.org
Licence: www.springer.com/tdm (tdm)
Locations: Nature reviews. Cancer (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute (U01CA190234, U01CA151118, U24CA194354)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: artificial intelligence in radiology, deep learning, medical image analysis, convolutional neural networks, variational autoencoder, image recognition, cancer detection, computer-aided diagnosis, clinical implementation barriers
MeSH terms: Machine Learning, Deep Learning, Algorithms, Artificial Intelligence, Humans, Image Processing, Computer-Assisted, Neoplasms, Pattern Recognition, Automated, Radiology

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