[HTML][HTML] Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …
derive insights from clinical data and improve patient outcomes. However, these highly …
[HTML][HTML] Human, all too human? An all-around appraisal of the “artificial intelligence revolution” in medical imaging
F Coppola, L Faggioni, M Gabelloni… - Frontiers in …, 2021 - frontiersin.org
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a
niche super specialty computer application into a powerful tool which has revolutionized …
niche super specialty computer application into a powerful tool which has revolutionized …
[HTML][HTML] Experimental evidence of effective human–AI collaboration in medical decision-making
Artificial Intelligence (ai) systems are precious support for decision-making, with many
applications also in the medical domain. The interaction between mds and ai enjoys a …
applications also in the medical domain. The interaction between mds and ai enjoys a …
[HTML][HTML] Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
Objective The objective was to identify barriers and facilitators to the implementation of
artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and …
artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and …
Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE guidelines)
An important need exists for strategies to perform rigorous objective clinical-task-based
evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need …
evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need …
Inconsistent performance of deep learning models on mammogram classification
Objectives Performance of recently developed deep learning models for image classification
surpasses that of radiologists. However, there are questions about model performance …
surpasses that of radiologists. However, there are questions about model performance …
[HTML][HTML] Machine learning and deep learning applications in multiple myeloma diagnosis, prognosis, and treatment selection
Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex
pathogenesis. With major progresses in multiple myeloma research, it is essential that we …
pathogenesis. With major progresses in multiple myeloma research, it is essential that we …
Computational radiology in breast cancer screening and diagnosis using artificial intelligence
Breast cancer screening has been shown to significantly reduce mortality in women. The
increased utilization of screening examinations has led to growing demands for rapid and …
increased utilization of screening examinations has led to growing demands for rapid and …
Deep learning applied to automatic disease detection using chest x‐rays
DA Moses - Journal of Medical Imaging and Radiation …, 2021 - Wiley Online Library
Deep learning (DL) has shown rapid advancement and considerable promise when applied
to the automatic detection of diseases using CXRs. This is important given the widespread …
to the automatic detection of diseases using CXRs. This is important given the widespread …
[HTML][HTML] Developing medical imaging AI for emerging infectious diseases
Advances in artificial intelligence (AI) and computer vision hold great promise for assisting
medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID …
medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID …