Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine
By Filippo Pesapane, M. Codari, Francesco Sardanelli. European Radiology Experimental, 2018
705 citations recorded by the source corpus.
One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/bce43a15423c7b1b049cfec2d923cb2352f646df
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In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis (2021, background): “learning (DL) is a new direction in ML, which is based on the simulating neural network structure of human brain to build a computational model (5, 6).”
- GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions (2023, background): “AI systems have the ability to analyse complex data, automate procedures, and make wise conclusions [100].”
- Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach (2020, background): “Anyway, regardless of the significance threshold, false discoveries in radiomics can be identified by external validation on at least one large independent dataset, an essential condition for declaring any diagnostic or prognostic value of machine/deep learning applications to clinical imaging [37].”