
AI outperform doctors: Experts express concerns
Many studies claiming that AI is as good as (or better than) human experts at interpreting medical images are of poor quality and are arguably exaggerated, warn researchers in The BMJ.
Many studies claiming that AI is as good as (or better than) human experts at interpreting medical images are of poor quality and are arguably exaggerated, warn researchers in The BMJ.
Currently, we are too focused on the topic of AI. In order, however, to leverage AI technology several challenges have to be mastered and a proper framework has to be established.
Scientists have developed a technique for visualising the structures of all the brain's blood vessels including any pathological changes.
An AI device may help identify newborns at risk for aggressive posterior retinopathy of prematurity (AP-ROP).
An AI model identifies a powerful new drug that can kill many species of antibiotic-resistant bacteria.
Usind deep learning and digital scanning of conventional hematoxylin and eosin-stained tumor tissue sections, researchers have developed a clinically useful prognostic marker.
A deep learning model can identify sleep stages as accurately as an experienced physician.
Researchers have designed a novel approach to use deep learning to better understand how proteins interact in the body.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
A deep neural network model helps predict healthcare visits by elderly people, with the potential to save millions.
Deep learning can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD).
Researchers have developed a new algorithm that enables automated detection of metastases at the level of single disseminated cancer cells in whole mice.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Radiologists assisted by deep learning based software were better able to detect malignant lung cancers on chest X-rays.
Researchers show that deep learning algorithms perform similar to human experts when classifying blood samples from patients suffering from acute myeloid leukemia.
Researchers have devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting.
An algorithm did better than experts radiologists at finding tiny brain hemorrhages in head scans — an advance that one day may help doctors treat patients with strokes.
Using a simple computer game and AI techniques, researchers were able to identify behavioural patterns in subjects with depression and bipolar disorder.
Researchers have applied deep learning techniques to develop a more accurate method for analysing images of the back of the eye.
Combining new wearable electronics and a deep learning algorithm could help disabled people wirelessly interact with a computer.
A research team has succeeded in identifying specific patterns in Electro-Encephalogram (EEG) analyses that the deep learning network uses for making prognosis decisions.
Researchers have developed a deep learning system that may help detecting diabetic eye diseases, which could make doctors’ work easier and reduce healthcare cost.
Stomach and colorectal cancer: Identifying patients at an early stage who are suitable for artificial intelligence immunotherapy.
Using artificial intelligence, researchers have decoded the functional impact of genome mutations in people with autism spectrum disorder.
With RAPID AI, the physicians now can get find parts of the brain that are not currently getting enough blood flow within minutes.
An AI approach can identify with high accuracy whether a 5-day-old, in vitro fertilized human embryo has a high potential to progress to a successful pregnancy.
The deep learning algorithm maps active neurons as accurately as humans in a fraction of the time.
Patients could soon get faster and more accurate diagnoses with new software that can automatically detect signs of diabetes, heart disease and cancer from medical images.
Researchers have developed a groundbreaking AI algorithm that can enable hearing aid users to take a more active part in conversations in noisy environments.
UCLA researchers have developed a rapid and automated biosensing method based on holography coupled with deep learning.
UCLA-led team produces images on a laptop that match the quality of those from high-end equipment.
SubtlePET’s AI-powered technology allows hospitals and imaging centers to enhance images from faster scans leading to an improved patient experience during imaging procedures.
Researchers are using artificial intelligence to reduce the dose of a contrast agent that may be left behind in the body after MRI exams, according to a study presented at RSNA.
Researchers are working on an interactive robot called Pharos that will help the elderly with their daily household chores.
Researchers have developed a novel system that can automatically detect abnormalities in fetal hearts in real-time using artificial intelligence (AI).