Machine learning

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Over the next decade

Robotics set to transform spinal surgery for millions

Robotics, AI, and machine learning can make spinal surgery more accurate, efficient, and safer, thereby reducing costs, patient recovery time, and radiation exposure.

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Discerning good algorithms from bad ones

Medical AI evaluation is surprisingly patchy, study finds

In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care…

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Shepherding medical data

Machine learning platform turns healthcare data into insights

Over the past decade, hospitals and other healthcare providers have put massive amounts of time and energy into adopting electronic healthcare records, turning hastily scribbled doctors' notes into…

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Model analysis

Machine learning for Covid-19 diagnosis: promising, but still too flawed

Systematic review finds that machine learning models for detecting and diagnosing Covid-19 from medical images have major flaws and biases, making them unsuitable for use in patients. However,…

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"Alexa, do I have an irregular heart rhythm?"

AI uses smart speakers for contactless cardiac monitoring

Smart speakers, such as Amazon Echo and Google Home, have proven adept at monitoring certain health care issues at home. For example, researchers at the University of Washington have shown that these…

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Potential for rapid, accurate glycan sequencing

Enormous boost for sequencing key molecules

Using a nanopore, researchers have demonstrated the potential to reduce the time required for sequencing a glycosaminoglycan — a class of long chain-linked sugar molecules as important to our…

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Survival prediction

Deep learning may lead to better lung cancer treatments

Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers. In a study, the researchers report that they developed a deep learning model that, in certain conditions, was more than 71% accurate in predicting survival expectancy of lung cancer…

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AI 'Haven' in intensive care

Machine learning to identify deteriorating hospital patients

Researchers in Oxford have developed a machine learning algorithm that could significantly improve clinicians’ ability to identify hospitalised patients whose condition is deteriorating to the extent that they need intensive care. The HAVEN system (Hospital-wide Alerting Via Electronic Noticeboard) was developed as part of a collaboration between the University of Oxford’s Institute of…

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Detecting depression, psychosis

Machine learning could aid mental health diagnoses

A way of using machine learning to more accurately identify patients with a mix of psychotic and depressive symptoms has been developed by researchers at the University of Birmingham. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a ‘primary’ illness, but with…

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Image-based diagnosis of Covid-19

AI detects coronavirus on CT scans

In order to detect the Corona virus SARS-CoV-2, there are further methods of diagnosis apart from the globally used PCR tests (Polymerase chain reaction): The infection can also be recognised on CT scans – for which Artificial Intelligence (AI) can be used as well. An AI system can not only filter CT scan of Covid-19 patients from a data set, but also estimate, which areas of the image are of…

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'Thumbs up' for image reconstruction

Facebook AI accelerates MRI exams

Artificial intelligence (AI) image reconstruction dramatically reduces magnetic resonance imaging (MRI) scan time, according to new research. The first clinical study comparing AI-accelerated knee MRI scans with conventional scans shows that the AI scans are not only diagnostically interchangeable with conventional ones, but also produce higher quality images. Results of this interchangeability…

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Machine learning advances diagnostics and prognostics

Computerized image analysis can predict cancer outcomes

The advent of digital pathology is offering a unique opportunity to develop computerized image analysis methods to diagnose disease and predict outcomes for cancer patients from histopathology tissue sections. Such advances can help predict risk of recurrence, disease aggressiveness and long-term survival, according to a leading expert in the field, Professor Anant Madabhushi from Case Western…

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Oversight of medical software

FDA Releases Artificial Intelligence/Machine Learning Action Plan

The U.S. Food and Drug Administration released the agency’s first Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. This action plan describes a multi-pronged approach to advance the Agency’s oversight of AI/ML-based medical software. “This action plan outlines the FDA’s next steps towards furthering oversight for AI/ML-based SaMD,”…

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Drug repurposing

AI finds new uses for existing medications

Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed. The intent of this work is to speed up drug repurposing, which is not a new concept – think Botox injections, first approved to treat crossed eyes and now a migraine treatment and top cosmetic…

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"InnerEye" Artificial Intelligence

AI could help cut waiting times for cancer radiotherapy

Doctors at Addenbrooke’s Hospital in Cambridge aim to drastically cut cancer waiting times by using artificial intelligence (AI) to automate lengthy radiotherapy preparations. The AI technology, known as InnerEye, is the result of an eight-year collaboration between researchers at Cambridge-based Microsoft Research, Addenbrooke’s Hospital and the University of Cambridge.

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Brain tumor treatment network

'Federated learning' AI approach allows hospitals to share patient data privately

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study…

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Glioma grading

AI enhances brain tumour diagnosis

A new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report in the journal IEEE Access. Scientists in India and Japan, including from Kyoto University’s Institute for Integrated Cell-Material Sciences (iCeMS), developed the method to help clinicians choose the most effective treatment strategy for individual…

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Identification of skin cancer

Machine learning challenge on melanoma classification

The Society for Imaging Informatics in Medicine (SIIM) and the International Skin Imaging Collaboration (ISIC) are working together to host a machine learning challenge on melanoma classification, using the ISIC archive which contains the largest publicly available collection of quality controlled dermoscopic images of skin lesions. Image contributors include: Hospital Clínic de Barcelona,…

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Algorithm-assisted diagnostics

AI in imaging: not as reliable as you'd think

Machine learning and AI are highly unstable in medical image reconstruction, and may lead to false positives and false negatives, a new study suggests. A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad…

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