Artificial intelligence in healthcare
Artificial intelligence (AI) is gradually changing medical practice. With recent progress in
digitized data acquisition, machine learning and computing infrastructure, AI applications …
digitized data acquisition, machine learning and computing infrastructure, AI applications …
Stillbirths: recall to action in high-income countries
Variation in stillbirth rates across high-income countries and large equity gaps within high-
income countries persist. If all high-income countries achieved stillbirth rates equal to the …
income countries persist. If all high-income countries achieved stillbirth rates equal to the …
Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study
Background An increasing volume of prostate biopsies and a worldwide shortage of
urological pathologists puts a strain on pathology departments. Additionally, the high intra …
urological pathologists puts a strain on pathology departments. Additionally, the high intra …
Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks
Intraoperative diagnosis is essential for providing safe and effective care during cancer
surgery. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin …
surgery. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin …
Impact of a deep learning assistant on the histopathologic classification of liver cancer
A Kiani, B Uyumazturk, P Rajpurkar, A Wang… - NPJ digital …, 2020 - nature.com
Artificial intelligence (AI) algorithms continue to rival human performance on a variety of
clinical tasks, while their actual impact on human diagnosticians, when incorporated into …
clinical tasks, while their actual impact on human diagnosticians, when incorporated into …
Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight
Automatic multi-classification of breast cancer histopathological images has remained one
of the top-priority research areas in the field of biomedical informatics, due to the great …
of the top-priority research areas in the field of biomedical informatics, due to the great …
Deep learning–based segmentation and quantification in experimental kidney histopathology
N Bouteldja, BM Klinkhammer, RD Bülow… - Journal of the …, 2021 - journals.lww.com
Background Nephropathologic analyses provide important outcomes-related data in
experiments with the animal models that are essential for understanding kidney disease …
experiments with the animal models that are essential for understanding kidney disease …
Trends in the US and Canadian pathologist workforces from 2007 to 2017
DM Metter, TJ Colgan, ST Leung… - JAMA network …, 2019 - jamanetwork.com
Importance The current state of the US pathologist workforce is uncertain, with deficits
forecast over the next 2 decades. Objective To examine the trends in the US pathology …
forecast over the next 2 decades. Objective To examine the trends in the US pathology …
Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study
Background Histopathological assessment of transplant biopsies is currently the standard
method to diagnose allograft rejection and can help guide patient management, but it is one …
method to diagnose allograft rejection and can help guide patient management, but it is one …
Automated acquisition of explainable knowledge from unannotated histopathology images
Y Yamamoto, T Tsuzuki, J Akatsuka, M Ueki… - Nature …, 2019 - nature.com
Deep learning algorithms have been successfully used in medical image classification. In
the next stage, the technology of acquiring explainable knowledge from medical images is …
the next stage, the technology of acquiring explainable knowledge from medical images is …