Subscribe to The Vet Blast Podcast on Apple Podcasts, Spotify, or wherever you get your podcasts. The applications of artificial intelligence (AI) in veterinary radiology are the subject of this episode of The Vet Blast Podcast with Seth Wallack, DVM, DACVR. Wallack explains how advances in AI are changing the game in radiology by improving efficiency with no changes in the clinician's workflow. Below is a partial transcript. Listen to the full podcast for more.
Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.
Artificial Intelligence plays an important role in Healthcare in various ways like brain tumor classification, medical image analysis, bioinformatics, etc. So if you are interested to learn AI for healthcare, I have collected 6 Artificial Intelligence Courses for Healthcare. I hope these courses will help you to learn Artificial Intelligence for healthcare. Before we move to the courses, I would like to explain the importance of Artificial Intelligence in the healthcare industry. According to the World Health Organization, there are 60% of cases where the health of an individual and their lifestyle are associated.
Deep learning (DL), also known as deep structured learning or hierarchical learning, is a subset of machine learning. It is loosely based on the way neurons connect to each other to process information in animal brains. To mimic these connections, DL uses a layered algorithmic architecture known as artificial neural networks (ANNs) to analyze the data. By analyzing how data is filtered through the layers of the ANN and how the layers interact with each other, a DL algorithm can'learn' to make correlations and connections in the data. These capabilities make DL algorithms an innovative tool with the potential to transform healthcare.
AI has made impressive strides in recent years, but it's still far from learning language as efficiently as humans. For instance, children learn that "orange" can refer to both a fruit and color from a few examples, but modern AI systems can't do this nearly as efficiently as people. This has led many researchers to wonder: Can studying the human brain help to build AI systems that can learn and reason like people do? Today, Meta AI is announcing a long-term research initiative to better understand how the human brain processes language. In collaboration with neuroimaging center Neurospin (CEA) and INRIA we're comparing how AI language models and the brain respond to the same spoken or written sentences.
Today, over 2/3 of the people on earth do not have access to radiologists. The are big disparities between counties and within countries. Some countries like the US have tens of thousands of radiologists whereas 14 African countries have no radiologists at all. In India there is approximately one radiologist for every 100,000 people whereas in the US there is one radiologist for every 10,000 people. There are also disparities within countries.
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that performed external validation, published from January 1, 2015 through April 1, 2021.
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Identifying intravenous (IV) contrast within CT scans is an important component of data curation for medical imaging-based, artificial intelligence (AI) model development and deployment. IV contrast is oftenpoorly documented in imagingmetadata, necessitating impractical ma nual annotation by clinician experts.
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs.
Every dog owner knows that saying Good dog! in a happy, high-pitched voice will evoke a flurry of joyful tail wagging in their pet. That made scientists curious: What exactly happens in your dog's brain when it hears praise, and is it similar to the hierarchical way our own brain processes such acoustic information? When a person gets a compliment, the more primitive, subcortical auditory regions first reacts to the intonation--the emotional force of spoken words. Next, the brain taps the more recently evolved auditory cortex to figure out the meaning of the words, which is learned. In 2016, a team of scientists discovered that dogs' brains, like those of humans, compute the intonation and meaning of a word separately--although dogs use their right brain hemisphere to do so, whereas we use our left hemisphere.