AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
Machine learning in healthcare communication
S Siddique, JCL Chow - Encyclopedia, 2021 - mdpi.com
Definition Machine learning (ML) is a study of computer algorithms for automation through
experience. ML is a subset of artificial intelligence (AI) that develops computer systems …
experience. ML is a subset of artificial intelligence (AI) that develops computer systems …
Natural language processing
SC Fanni, M Febi, G Aghakhanyan, E Neri - Introduction to Artificial …, 2023 - Springer
Natural language processing (NLP) stands halfway between computer science
computational linguistics, and it is dedicated to the conversion of written and spoken natural …
computational linguistics, and it is dedicated to the conversion of written and spoken natural …
Basic of machine learning and deep learning in imaging for medical physicists
L Manco, N Maffei, S Strolin, S Vichi, L Bottazzi… - Physica Medica, 2021 - Elsevier
The manuscript aims at providing an overview of the published algorithms/automation tool
for artificial intelligence applied to imaging for Healthcare. A PubMed search was performed …
for artificial intelligence applied to imaging for Healthcare. A PubMed search was performed …
Applications of natural language processing in radiology: A systematic review
N Linna, CE Kahn Jr - International Journal of Medical Informatics, 2022 - Elsevier
Background Recent advances in performance of natural language processing (NLP)
techniques have spurred wider use and more sophisticated applications of NLP in radiology …
techniques have spurred wider use and more sophisticated applications of NLP in radiology …
Harnessing artificial intelligence in radiology to augment population health
JZT Sim, KN Bhanu Prakash, WM Huang… - Frontiers in medical …, 2023 - frontiersin.org
This review article serves to highlight radiological services as a major cost driver for the
healthcare sector, and the potential improvements in productivity and cost savings that can …
healthcare sector, and the potential improvements in productivity and cost savings that can …
Artificial intelligence applications for workflow, process optimization and predictive analytics
L Letourneau-Guillon, D Camirand… - Neuroimaging …, 2020 - neuroimaging.theclinics.com
Applications of artificial intelligence (AI) in health care, especially machine learning, have
garnered significant attention in the last decade. In radiology, advances in machine learning …
garnered significant attention in the last decade. In radiology, advances in machine learning …
Perceptions of radiologists on structured reporting for cancer imaging—a survey by the European Society of Oncologic Imaging (ESOI)
D Leithner, E Sala, E Neri, HP Schlemmer… - European …, 2024 - Springer
Objectives To assess radiologists' current use of, and opinions on, structured reporting (SR)
in oncologic imaging, and to provide recommendations for a structured report template …
in oncologic imaging, and to provide recommendations for a structured report template …
[HTML][HTML] Natural language processing to convert unstructured COVID-19 chest-CT reports into structured reports
SC Fanni, C Romei, G Ferrando, F Volpi… - European Journal of …, 2023 - Elsevier
Background Structured reporting has been demonstrated to increase report completeness
and to reduce error rate, also enabling data mining of radiological reports. Still, structured …
and to reduce error rate, also enabling data mining of radiological reports. Still, structured …
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning
Background There is progress to be made in building artificially intelligent systems to detect
abnormalities that are not only accurate but can handle the true breadth of findings that …
abnormalities that are not only accurate but can handle the true breadth of findings that …