Cedars-Sinai investigators are using AI to predict and diagnose disease earlier and more accurately. Below we highlight a few of the latest advances from our researchers in this dynamic field.

AI accurately identifies increased buildup of coronary artery calcium, a marker of coronary artery disease, and shows promise for predicting often-fatal sudden cardiac arrest.

AI speeds analyses and yields more accurate diagnoses of COVID-19 pneumonia, which can be challenging to classify.

A new tool that accurately predicts reduced coronary blood flow could potentially be used in routine patient visits to determine next steps in the treatment plan.

An innovative computer program may predict whether a woman will deliver vaginally or via a cesarean section.

The accuracy of diagnosing coronary artery disease and predicting patient risk is improved with the help of AI models.

An $8 million grant from the National Institutes of Health uses AI and machine-learning methods to identify genetic predictors of Alzheimer’s disease.

AI proved more successful in assessing and diagnosing cardiac function when compared to echocardiogram assessments made by sonographers.

AI and machine learning can help spine surgeons predict which patients might struggle with opioids to address pain-management issues in advance.

A smartphone app’s AI demonstrated accuracy that matched expert gastroenterologists at characterizing stool specimens.

AI technology accurately predicted who would develop pancreatic cancer based on what their CT scans looked like years prior to being diagnosed with the disease.

A clinical algorithm is the first to distinguish between treatable and untreatable sudden cardiac arrest, holding the potential to enhance prevention of the often-deadly condition.

An AI-enabled tool may make it easier to predict whether a person will have a heart attack based on the amount and composition of plaque in arteries that supply blood to the heart.

An AI algorithm can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis.

Virtual replicas of patients’ DNA, RNA, protein and other information help design personalized cancer treatment options and risk assessment.