Researchers work on the first prototype that applies AI to colorectal diagnosis. The prototype achieved a diagnostic acuity of 93.44% and a sensitivity of 99.7% in the detection of high-risk lesions.
Not all medication can safely be taken together. Using a machine-learning algorithm, researchers predict interactions that could interfere with a drug’s effectiveness.
AI models are highly capable in analysing tissue samples – as long as conditions are lab-perfect. Add a little contamination, however, and diagnostic accuracy goes out the window, a new study shows.
Researchers have developed software able to disguise sensitive data in health care applications. This protects privacy while making datasets available for development of better treatments.
A feasibility study by Swiss researchers shows that AI systems are able to reliably predict therapeutic success in patients with borderline personality pathology by detecting emotions on their face.
Can AI help better evaluate images of brain tumours? A publication from German researchers on this topic presented at this years' ICIS conference won the Best Paper Award.
A powerful tool, but the need for human judgment remains: In an editorial published in PNAS Nexus, Monica M. Bertagnolli assesses the promise of AI and machine learning to study and improve health.
Sure, AI still has a long way go. But maybe one day in the not-so-distant future, AI will provide us with information about our current state of health, such as the number of red blood cells, cholesterol levels, fat percentage, and how many seconds last night's beer will shorten our life expectancy.
Machine learning and AI are playing an increasingly important role in medicine and healthcare, and not just since ChatGPT. This is especially true in data-intensive specialties such as radiology, pathology or intensive care. The quality of diagnostics and decision-making via AI, however, does not only depend on a sophisticated algorithm but – crucially – on the quality of the training data.
Researchers from Rutgers University developed a way to help hospitals identify life-threatening Covid-19 cases using machine-learning software. The algorithm identified six crucial parameters.
The 2023 AACC meeting saw two exciting AI applications in lab medicine: a predictive algorithm for MS, and machine learning for detecting contaminated lab samples.
Scientists have designed an AI tool that can rapidly decode a brain tumor’s DNA to determine its molecular identity during surgery — critical information that can guide treatment decisions.
Machine-learning algorithms are 13% more accurate in predicting the surgical time needed in the operating room compared with human schedulers, according to new US research.
An algorithm developed using artificial intelligence could soon be used by doctors to diagnose heart attacks with better speed and accuracy than ever before, according to new research.
An artificial intelligence developed at TU Wien (Vienna) can suggest appropriate treatment steps in cases of blood poisoning. The computer has already surpassed humans in this respect.
Autism spectrum disorder (ASD) is a developmental disorder associated with difficulties in interacting with others, repetitive behaviors, restricted interests and other symptoms that can impact academic or professional performance.
A UK research team has developed a new technique that combines machine learning with short-wave infrared (SWIR) fluorescence imaging to detect precise boundaries of tumors.
Predicting a Covid-19 infection from the sound of a cough? Researchers found that technology using Machine Learning performed no better than simply asking people to report their symptoms.
US researchers are developing a highly accurate machine learning model for early detection of mild cognitive impairment and dementia in older drivers.
