Combining AI with advances in acoustic engineering, Australian researchers have developed an algorithm that can help diagnose common pediatric respiratory conditions such as asthma, croup and pneumonia.

Stanford researchers have developed an AI tool that can help diagnose damaging brain aneurysms that can have potentially fatal effects.

Using a deep neural network equipped with all patient information relevant to diagnosing adult asthma, researchers in Japan have achieved 98% diagnostic accuracy.

Researchers have developed a deep-learning framework that can show how mutations in “noncoding DNA”—meaning parts of the strand that contain no genes—contribute to autism. And they believe their algorithm is generalizable for clinical researchers studying the role of noncoding mutations in just about any disease.

Researchers in China have developed a deep learning algorithm able to diagnose hyperlipidemia—elevated levels of cholesterol, fats and triglycerides in the bloodstream—in both blood and urine specimens, potentially giving clinicians more information with less expense to the patient.  

Deep learning can help diagnose skin cancer with high accuracy even when it has only low-tech dermoscopic images to work with, according to research conducted in Israel and published in The Lancet’s online journal eBioMedicine.

An AI algorithm created by Google can predict lung cancer with high accuracy and improve the survival chances of those with the cancer through earlier diagnosis, according to a recent study. The findings were published in Nature Medicine on May 20.

Contrary to common understanding, sepsis is not a one-size-afflicts-all condition. It’s a set of many, and each can be placed into one of four key subcategories—i.e., phenotypes—such that, going forward, stricken patients could receive tailored treatments.

Instead of Googling symptoms to when feeling an ailment and landing on an incorrect diagnosis, AI could soon provide accurate diagnosis without needing to go to a doctor’s office.

AI can speed up precise detection of one of the key signs of Alzheimer’s disease, according to researchers from University of California Davis and UC San Francisco, who published a study on their machine learning tool in Nature Communications.

The simultaneous advances of deep learning and radiomics may soon yield a single unified framework for clinical decision support that has the potential to “completely revolutionize the field of precision medicine.”

The FDA has given 510(k) clearance to an AI alert for urgent finding of a collapsed lung in chest X-rays. The approval is a first for an AI-based chest X-ray solution that can help doctors make quicker diagnoses from one of the world’s most used imaging modalities.