Artificial intelligence (AI) machine learning is a rapidly emerging brain modeling tool for mental health research, psychiatry, neuroscience, genomics, pharmaceuticals, life sciences, and biotechnology. Scientists have identified areas of potential weak spots in AI brain models and offer solutions on how to prevent bias in a new peer-reviewed study. The research team led by Abigail Greene at Yale School of Medicine along with co-authors affiliated with Yale University, Brigham and Women's Hospital, Harvard Medical School, University of Washington, and Columbia University Irving Medical Center's Department of Psychiatry points out the need to identify why AI algorithms for brain models do not work for everyone when seeking to understand brain-phenotype relationships without biases. "Individual differences in brain functional organization track a range of traits, symptoms and behaviors," wrote the scientists. "So far, work modelling linear brain–phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants."
I recently built a brain MRI segmentation project, that segments out tumors from MRI scans with 93% accuracy. In this article, however, I will be diving deeper into the open-source dataset that I used. This dataset was talked about in a research paper that I discuss in this article and has been linked to the bottom of the page. The Kaggle contributor for this particular dataset is Mateusz Buda, who is a Senior Machine Learning Engineer at IQVIA. The dataset was obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA).
Whether we're ready or not, artificial intelligence (AI) already plays a role in many health care settings. However, cautiously developing, deploying, and even defining further AI advancements will determine its impact and efficacy in the years ahead, according to a new University of Western Ontario study. Interdisciplinary researchers from family medicine, computer science, and epidemiology have identified key issues regarding the use of AI tools in primary health care by connecting directly with family physicians, nurses, nurse practitioners and digital health stakeholders. Overwhelmingly, the responses show AI could have a positive impact in clinical practice, but many factors must be considered regarding its implementation. "We are ready for AI, but we must be thoughtful about how and when we use it," said Dan Lizotte, an associate professor in computer science and the Schulich School of Medicine & Dentistry and senior author on the study.
This article was published as a part of the Data Science Blogathon. Artificial Intelligence (AI) has made a great impact across a wide range of industries, especially healthcare. This blog covers some of the applications of AI in the healthcare sector. Many hospitals use robotic technology to help them complete tasks that call for accuracy, control, and flexibility. It is used for very complex tasks requiring excellent skills and accuracy.
At RE•WORK, we are strong advocates for supporting women working towards advancing technology, so ahead of the upcoming AI in Healthcare Summit, we set out to highlight inspirational women within the US healthcare and pharma sectors who are working at the forefront of AI developments, and who deserve recognition for their achievements. While we set out to create a list of just 20 – we couldn't narrow it down, as there are so many inspiring and prominent females in this space! Hear from many of them at our AI in Healthcare Summit, and more outside the healthcare space at our Women in AI Reception, both being held in Boston next month. Help us to continue highlighting leading women in AI by nominating your influential woman for our next edition. RE•WORK holds Women in AI events, podcasts, and blogs.
A team of U-M researchers, including Prof. Jenna Wiens, has been awarded a $9.2 million grant to tackle Clostridium difficile. Clostridium difficile, also called C. difficile or C. diff, is a bacterium that can cause symptoms ranging from diarrhea to life-threatening inflammation of the colon. C. difficile is difficult to eradicate and is often transmitted to patients in hospital environments. The researchers were awarded the grant from the National Institutes of Health as a government backed effort to attack antibiotic resistant bacteria. They will spend the next five years studying this pathogen that kills over 14,000 each year.
Deep learning (DL) has seen an enormous increase in popularity in various fields. DL has been used for brain-computer interfaces (BCIs) with electroencephalography (EEG) as well. However, DL models needed to be adapted for EEG data. How has this been done, and how successful are DL approaches in the field of BCIs? In this post, we first explain why DL can be advantageous compared to the traditional machine learning (ML) methods for BCIs.
Welcome to State of Mind, a new section from Slate and Arizona State University dedicated to exploring mental health. When parents learn about Michael Milham's research, they often ask him, "Can you give my child a brain scan to figure out what's wrong with them?" Milham treats his young patients like any other child psychiatrist would: He observes and interviews them, assigns them diagnoses, and prescribes courses of treatment. But unlike many psychiatrists, Milham is also a scientist--he is vice president of research at the Child Mind Institute--and an expert on functional magnetic resonance imaging, or fMRI, a tool that allows researchers to measure levels of activity across the brain. He understands why parents want him to scan their children's brains. For families in search of an explanation for their child's distress, the inexactitude of psychiatry--its overlapping diagnoses, its uncertain prognoses--can be frustrating.
The Colorado State Fair recently held its annual art competition, and one of the top prizes went to an AI-generated image created by Jason Allen. He used a platform called Midjourney to turn text into a work of art. Artists of the paint-and-canvas variety are up in arms about the whole thing, astonished that somebody who typed a string of words on their keyboard and hit "enter" is now considered a member of their community. Regardless of which side you land on the debate, you can't deny that in the past year, the world of AI art -- and technology in general -- has moved incredibly fast. Not only do we have NFTs and Shiba Inu coins, but we also now have DALL-E, robots that run restaurants and smart contact lenses (more on that later).
XRHealth, the gateway to the healthcare metaverse, announces that the company adds NeuroReality's cognitive training to their virtual clinics. The NeuroReality's virtual reality neurorehab, a serious game, is an immersive experience known as Koji's Quest. It was designed for individuals who suffer from the consequences of stroke and brain injuries, where patients are guided through activities aimed to help regain functionality in their everyday lives. "We are constantly adding state-of-the-art virtual reality therapeutic programs for our users so they can have a one-stop-shop for all their rehabilitation needs," says Eran Orr, Founder & CEO of XRHealth. "We find that patients enjoy the game-like therapy experiences and are more likely to stick with the prescribed programs since they are engaging from the comfort of their home."