[HTML][HTML] Understanding of machine learning with deep learning: architectures, workflow, applications and future directions
MM Taye - Computers, 2023 - mdpi.com
In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …
the field of machine learning (ML), achieving exceptional results on a variety of complex …
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 …
[HTML][HTML] Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models
TH Kung, M Cheatham, A Medenilla, C Sillos… - PLoS digital …, 2023 - journals.plos.org
We evaluated the performance of a large language model called ChatGPT on the United
States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK …
States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK …
Laion-5b: An open large-scale dataset for training next generation image-text models
C Schuhmann, R Beaumont, R Vencu… - Advances in …, 2022 - proceedings.neurips.cc
Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of
training on large amounts of noisy image-text data, without relying on expensive accurate …
training on large amounts of noisy image-text data, without relying on expensive accurate …
Towards generalist biomedical AI
Background Medicine is inherently multimodal, requiring the simultaneous interpretation
and integration of insights between many data modalities spanning text, imaging, genomics …
and integration of insights between many data modalities spanning text, imaging, genomics …
[HTML][HTML] 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 …
[HTML][HTML] Shifting machine learning for healthcare from development to deployment and from models to data
In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …
the automation of physician tasks as well as enhancements in clinical capabilities and …
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 …
External validation of deep learning algorithms for radiologic diagnosis: a systematic review
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
diagnosis. Materials and Methods In this systematic review, the PubMed database was …
[HTML][HTML] Opening the black box: the promise and limitations of explainable machine learning in cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …