AI applications to medical images: From machine learning to deep learning

I Castiglioni, L Rundo, M Codari, G Di Leo, C Salvatore… - Physica medica, 2021 - Elsevier
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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

[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 …

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning

VM D'Anniballe, FI Tushar, K Faryna, S Han… - BMC medical informatics …, 2022 - Springer
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 …