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Essential Questions for Assessing Artificial Intelligence Vendors in Radiology

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What are the key questions radiologists should ask when assessing artificial intelligence (AI) vendors? While the list can be long, one important question is ascertaining the volume and nature of the data used to develop and train a given AI algorithm, according to Sonia Gupta, MD, an abdominal radiologist, and chief medical officer at Change Healthcare. In a recent video interview, Dr. Gupta said knowing the volume of cases that went into the training of an AI model is an important consideration as is the diversity of that data in terms of factors such as age, gender, health issues and comorbidities to name a few. "All of those factors will influence the model training and ultimately the performance of that AI algorithm," noted Dr. Gupta, who lectured about AI at the recent Society for Imaging Informatics in Medicine (SIIM) conference. "I encourage radiologists looking at potential AI vendors to dig into that information right off the bat."


The Future of Healthcare: MIT, Bayer & Others Leverage AI, Machine Learning

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From search engines to social media, algorithms are quickly becoming a part of everyday life, and companies and academic institutions like Bayer, the Massachusetts Institute of Technology (MIT) and others are using them to their advantage. In artificial intelligence (AI) and machine learning (ML), algorithms are used to solve complex problems - including in preventative healthcare, diagnostics and drug discovery. Continue reading to learn how these technologies are being utilized in the life sciences. One of the most time-consuming diagnostic tools available is radiology. First, the patient sits tight for the required timeframe, before a radiologist sits down to analyze the captured images.


Radiology: Artificial Intelligence

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Nooshin Abbasi is a post-doctoral research fellow at Brigham and Women's Hospital, Harvard Medical School, and a former research fellow at the Montreal Neurological Institute, McGill University. Her research interests include brain imaging, evidence-based imaging, and bioinformatics, with a focus on applying machine learning tools to large clinical and imaging datasets. Michael Dohopolski is a PGY5 radiation oncology resident. He has worked with Dr. Wang and Dr. Jiang at UT Southwestern on machine learning based clinical decision-making support tools with an emphasis on single prediction uncertainty estimation. She is in the Department of Neurosurgery, University of Pennsylvania, and Division of Neurosurgery, Children's Hospital of Philadelphia.


RadBERT: Adapting Transformer-based Language Models to Radiology

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"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 investigate if tailoring a transformer-based language model to radiology is beneficial for radiology natural language processing (NLP) applications. This retrospective study presents RadBERT, a family of bidirectional encoder representations from transformers-based language models adapted for radiology.


Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium Contrast: A Multicenter, Multivendor Study

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"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 develop automated vestibular schwannoma measurements on contrast-enhanced T1-and T2-weighted MRI. MRI data from 214 patients in 37 different centers was retrospectively analyzed between 2020–2021.


Neural network generates lung ventilation images from CT scans – Physics World

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Incorporating ventilation images into radiotherapy plans to treat lung cancer could reduce the incidence of debilitating radiation-induced lung injuries, such as radiation pneumonitis and radiation fibrosis. Specifically, ventilation imaging can be used to adapt radiation treatment plans to reduce the dose to high-functioning lung. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) scans are the gold standard of ventilation imaging. However, these modalities are not always readily available and the cost of such exams may be prohibitive. As such, researchers are investigating the feasibility of alternatives such as MR or CT ventilation imaging.


Focus on machine learning models in medical imaging – Physics World

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Join the audience for an AI in Medical Physics Week live webinar at 3 p.m. BST on 23 June 2022 based on IOP Publishing's special issue, Focus on Machine Learning Models in Medical Imaging Want to take part in this webinar? An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research. Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research.


Bridging the knowledge gap on AI and machine-learning technologies – Physics World

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How much is too much? These are questions that cut to the heart of a complex issue currently preoccupying senior medical physicists when it comes to the training and continuing professional development (CPD) of the radiotherapy physics workforce. What's exercising management and educators specifically is the extent to which the core expertise and domain knowledge of radiotherapy physicists should evolve to reflect – and, in so doing, best support – the relentless progress of artificial intelligence (AI) and machine-learning technologies within the radiation oncology workflow. In an effort to bring a degree of clarity and consensus to the collective conversation, the ESTRO 2022 Annual Congress in Copenhagen last month featured a dedicated workshop session entitled "Every radiotherapy physicist should know about AI/machine learning…but how much?" With several hundred delegates packed into Room D5 at the Bella Center, speakers were tasked by the session moderators with defending a range of "optimum scenarios" to align the know-how of medical physicists versus emerging AI/machine-learning opportunities in the radiotherapy clinic.


Can Artificial Intelligence Improve Diagnosis in Radiology? - Digital Salutem

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Artificial Intelligence (AI) is already helping doctors and medical professionals in a variety of ways. AI can help diagnose diseases, identify genetic risk factors, and even predict how patients will respond to certain drugs. But could AI be used to improve radiology? In the early days of radiography, radiology was a part of medicine. Doctors used x-rays to diagnose and treat a large variety of illnesses, from arthritis to cancer.


Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study

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"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 conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an "interpretable (artificial intelligence) AI" model to detect COVID-19 on chest radiographs (CXRs). The model was deployed as a clinical decision support system, and performance was prospectively evaluated.