Artificial intelligence in radiology

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

3,232 citations recorded by the source corpus.

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. In this Opinion article, Hosny et al. discuss the application of artificial intelligence to image-based tasks in the field of radiology and consider the advantages and challenges of its clinical implementation.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b9ede5f604668d0b62a306392cd03f47086e245e

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Citations in the corpus

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “For instance, in oncology, AI algorithms can sift through radiographic images, genetic information, and patient histories to detect cancer at early stages [12].”
Harnessing multimodal data integration to advance precision oncology (2021, background): “Deep learning is a subtype of ml that has the potential to learn more informative features than engineered features, but there is difficulty in model interpretability, and performance is notoriously dependent on the amount of training data available 14 .”
How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications (2023, background): “Deep learning models, trained on large datasets, are capable of recognizing complex patterns and features that may not be readily discernible to the human eye [2,3].”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “thoracic (lung cancer); abdominal and pelvic (computed tomography (CT) and magnetic resonance imaging (MRI)); colonoscopy (colonic polyps); mammography (microcalcifications); brain (brain tumors); radiation oncology (segmenting tumors for radiation, and quantifying specific radiographic characterist”
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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, positive): “Similar ideas have been explored in other medical imaging domains, such as prediction of survival from computer tomography images 42,43 and prediction of molecular markers from echocardiography images 44 .”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, neutral): “…with their learning environments (Maseleno et al., 2018), and developing new processes such as that of decision-aiding tools for detection, characterization and monitoring of diseases in image-recognition tasks related to radiology for instance (Hosny, Parmar, Quackenbush, Schwartz, & Aerts, 2018).”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “…some stems from computational analyses, such as systems that can improve doctors ’ interpretation and decision-making about radiologic images (Hosny, Parmar, Quackenbush, Schwartz, & Aerts, 2018), or machine-learning algorithms that can predict customer preferences (Boyle, 2018; Gomez-Uribe…”
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging (2019, background): “Artificial intelligence (AI) allows for a quantitative, instead of a qualitative, assessment of radiographic tumor characteristics, a process also referred to as "radiomics" (6).”
The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. (2019, positive): “6,20 In the field of dental and maxillofacial radiology (DMFR), pre-clinical studies have been reporting on AI diagnostic models to exactly locate root canal orifices 21,22 and detect vertical root fractures 23 and proximal dental caries 24 with generally favorable findings.”
Deep learning in medical image registration: a survey (2019, background): “Further, these operations can be performed patch-wise, which is useful for a number of computer vision tasks [49, 141].”
Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization (2020, background): “tions including brain tumor, lungs nodule, pneumonia, breast cancer detection, physiological monitoring and social sensing [13]–[19].”