Health & Medicine


How far can we trust AI in clinical trials?- INDUSTRY VOICES

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My views are similar to Jenny's here. It also depends what you mean by AI as it's a rather hyped term and I have sat in a meeting and heard someone describe it as'superior intelligence', which it is not. Machine learning and algorithms might be more appropriate descriptions and here the challenge is transparency and trust. We need to be confident that ultimately there is a clinician making an informed judgement in a patients best interest. That requires oversight as well as understanding how the algorithm works, the decision tree and the dataset used to develop/train the algorithms.


Artificial intelligence examining ECGs may predict mortality, AF

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Deep neural networks identified potential adverse outcomes and atrial fibrillation from 12-lead ECGs that were originally interpreted as normal, according to new research presented at the American Heart Association Scientific Sessions. "Applications of machine learning and artificial intelligence techniques to problems in health care are increasingly common, but generally focus on diagnostic problems such as detecting features in an image of classifying a current diagnosis based on present features," Christopher M. Haggerty, PhD, assistant professor in the department of imaging science and innovation, and Brandon K. Fornwalt, MD, PhD, associate professor and director of the department of imaging science and innovation, both at Geisinger in Danville, Pennsylvania, told Healio. "Few studies have been able to apply machine learning to the task of predicting future events or patient outcomes. This work is among the first to demonstrate proof of concept for predicting a future patient event -- 1-year mortality -- with good performance based solely on 12-lead electrocardiography data." Sushravya M. Raghunath, PhD, math and computational scientist in the department of imaging science and innovation at Geisinger, and colleagues analyzed 1,775,926 12-lead resting ECGs of 397,840 patients from 34 years of archived medical records.


Researchers develop an AI system with near-perfect seizure prediction

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While it's not a complete fix, the new AI system, developed by Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette, is a major leap forward from existing prediction methods. Currently, other methods analyze brain activity with an EEG (electroencephalogram) test and apply a predictive model afterwards. The new method does both of those things at once, with the help of a deep learning algorithm that maps brain activity and another that can predict the electrical channels lighting up during a seizure. It'll still be some time before this technique will be available for widespread use -- the team is now working on a custom chip that can help process the necessary algorithms -- but it could be life-changing news for patients with epilepsy.


Researchers develop an AI system with near-perfect seizure prediction

#artificialintelligence

While it's not a complete fix, the new AI system, developed by Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette, is a major leap forward from existing prediction methods. Currently, other methods analyze brain activity with an EEG (electroencephalogram) test and apply a predictive model afterwards. The new method does both of those things at once, with the help of a deep learning algorithm that maps brain activity and another that can predict the electrical channels lighting up during a seizure. It'll still be some time before this technique will be available for widespread use -- the team is now working on a custom chip that can help process the necessary algorithms -- but it could be life-changing news for patients with epilepsy.



ICIPRoB2020

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Through several waves of downhills and uphills in the past decades, Artificial Intelligence (AI) has now evolved into a must have new technology or tool in every domain. Furthermore, with the advent of powerful GPU, AI-related research or AI-based applications have sprouted in every corner of the world. Originated from pure internet connectivity the Internet of Things (IoT) has become a structure that can collect every piece of data from physical devices, daily activities, images or video into a data reservoir. As a result, tons of data are automatically generated into an enterprise database in a single day. This creates continuing demands on applying AI, IoT, and big data analytics to extract juicy contents from the huge databases.


Woman seeking treatment for dizziness finds out she's missing her cerebellum

FOX News

Despite being born without this essential part, the woman learned to walk and talk, although her mother reported she learned these actions around age 6 and 7. (iStock) In 2014, a Chinese woman in her 20's sought treatment for recurring problems with balance and dizziness, reports a case study published in the journal Brain. But when doctors looked at brain imaging via a CT scan and MRI, they discovered their patient was living without her cerebellum. This young woman falls within a small group of nine people diagnosed with cerebellar agenesis, the study reports. Despite being born without this essential part, the woman learned to walk and talk, although her mother reported she learned these actions around age 6 and 7. However, the young woman had always struggled with walking steadily and had some trouble pronouncing words, according to the study.


6 AI Healthcare Solutions for Remote Patient Monitoring

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It's no secret that big tech companies like Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOG), the parent company of Google, are investing in digital healthcare. The market opportunity is pretty enticing when you consider that the U.S. alone spent $3.65 trillion on healthcare just last year. Google made the latest headline-grabbing move when it announced that it would buy wearables maker Fitbit (FIT) in a deal valued at $2.1 billion. Analysts have noted that the acquisition is part of the company's overall strategy to build an ambient intelligent system where Google is omnipresent. Another motive behind the purchase – pending regulatory approvals – is that Fitbit gives Google access to a treasure trove of healthcare data that it can feed to its London-based AI lab DeepMind or its life sciences subsidiary Verily, which is already collaborating on at least one AI healthcare device for remote patient monitoring.


DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

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Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90–0.91)


Investors just poured more than $1 billion into startups using AI to tackle every part of healthcare. Here are the 5 healthcare AI startups raking in the most cash.

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Investors just poured a record sum of cash into startups looking to use artificial intelligence (AI) to change healthcare. In the third quarter, healthcare AI companies raised almost $1.6 billion across 103 deals, according to a new report from CB Insights. That's a record sum, and an increase from the $749 million healthcare AI startups took in a year ago. Global deal count is on pace to reach an all-time high, according to the CB Insights report. And in total, healthcare companies have raised over $37.5 billion so far this year, the report says.