Dr. Frank Phillips, Professor and Director of the Division of Spine Surgery and the Section of Minimally Invasive Spine Surgery at Rush University Medical Center, completed the first augmented reality (AR) minimally invasive spine surgery.
Researchers are collaborating with local partners to establish a network of portable, handheld ultrasound scanners that can soon accelerate COVID-19 diagnosis.
Students at Cranfield University have designed computer models that can identify COVID-19 in X-rays.
Researchers have developed an AI algorithm that can detect and identify different types of brain injuries.
Establishing whether a patient is suffering from COVID-19 within a few minutes is possible using ultrasound machines that are enhanced with artificial intelligence.
Radiologists are investigating people's medical conditions and pregnancies remotely thanks to an ESA-backed robotic technology.
Researchers have developed a predictive artificial intelligence model that can tell the difference between healthy patients, those who are ill with pneumonia and those who have COVID-19, from chest X-rays.
A study showed that an AI algorithm provides results comparable with lung function tests, which measure how forcefully a person can exhale.
Researchers have developed a new model that accurately and automatically shows the exact location of mandibular canals.
Augmented reality ultrasound has, for the first time, made it possible to superimpose topographical representations of ultrasound images directly on a patient, with the examiner seeing the sectional image in AR glasses.
Brain cancer patients in the coming years may not need to go under the knife to help doctors determine the best treatment for their tumors.
Researchers have presented a method that could greatly accelerate dynamic magnetic resonance imaging of blood flow.
Researchers are developing a new high-precision radiology system for coronavirus pulmonary involvement.
behold.ai has been issued with a CE Mark Class lla certification in the UK and EU for its AI-based technology that can diagnose chest X-rays as ‘normal’.
Scientists have developed an innovative new technique that uses artificial intelligence to better define the different sections of the brain in newborns during a magnetic resonance imaging (MRI) exam.
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.
AI techniques, used in combination with the evaluation of expert radiologists, improve the accuracy in detecting cancer using mammograms.
Researchers demonstrated a methodology that combines the bioprinting and imaging of glioblastoma cells in a way that more closely models what happens inside the human body.
Engineers have created a tabletop device that combines a robot, AI and near-infrared and ultrasound imaging to draw blood or insert catheters to deliver fluids and drugs.
New machine learning study suggest the presence of at least nine gender “expressions”.
In a new study, artificial intelligence has been used for the first time to instantly and accurately measure blood flow.
Artificial intelligence may soon play a critical role in choosing which depression therapy is best for patients.
Researcher have developed a computer method that uses MRI and machine learning to rapidly forecast genetic mutations in glioma tumors,
A computer algorithm has been shown to be as effective as human radiologists in spotting breast cancer from x-ray images.
Deep learning can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD).
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
Researchers have developed a new method to guide endovascular instruments into complex vascular structures that were inaccessible to endovascular surgeons until now.
Scientists can determine which lung-cancer patients will benefit from expensive immunotherapy.
Ensembles created using models submitted to the RSNA Pediatric Bone Age Machine Learning Challenge convincingly outperformed single-model prediction of bone age.
