The current and future state of AI interpretation of medical images
P Rajpurkar, MP Lungren - New England Journal of Medicine, 2023 - Mass Medical Soc
The Current and Future State of AI Interpretation of Medical Images | New England Journal of
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Medicine Skip to main content The New England Journal of Medicine homepage Advanced …
Machine learning for medical imaging: methodological failures and recommendations for the future
G Varoquaux, V Cheplygina - NPJ digital medicine, 2022 - nature.com
Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …
health. However, a number of systematic challenges are slowing down the progress of the …
A foundation model for generalizable disease detection from retinal images
Medical artificial intelligence (AI) offers great potential for recognizing signs of health
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
AI in health and medicine
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …
of clinical experts. However, in settings differing from those of the training dataset, the …
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 …
Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers
Study Design Item 5. Indicate if the study is retrospective or prospective. Evaluate predictive
models in a prospective setting, if possible. Item 6. Define the study's goal, such as model …
models in a prospective setting, if possible. Item 6. Define the study's goal, such as model …
[HTML][HTML] Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions
Abstract The Internet of Medical Things (IoMT) has created a wide range of opportunities for
knowledge exchange in numerous industries. The opportunities include patient …
knowledge exchange in numerous industries. The opportunities include patient …
Deep learning image reconstruction for CT: technical principles and clinical prospects
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …