A new way of using artificial intelligence to predict cancer from patient data without putting personal information at risk has been developed by a team including University of Leeds medical scientists.

Artificial intelligence (AI) can analyse large amounts of data, such as images or trial results, and can identify patterns often undetectable by humans, making it highly valuable in speeding up disease detection, diagnosis and treatment.

In a small but multi-institutional study, an artificial intelligence (AI)-based system improved providers' assessments of whether patients with bladder cancer had complete response to chemotherapy before a radical cystectomy (bladder removal surgery).

Yet the researchers caution that AI isn't a replacement for human expertise and that their tool shouldn’t be used as such.

Artificial intelligence reduced by twofold the rate at which precancerous polyps were missed in colorectal cancer screening, reported a team of international researchers led by Mayo Clinic. The study is published in Gastroenterology.

Most colon polyps are harmless, but some over time develop into colon or rectal cancer, which can be fatal if found in its later stages.

A new digital health study by researchers at Scripps Research shows how data from wearable sensors, such as smartwatches and fitness bands, can track a person’s physiological response to the COVID-19 vaccination.

The study, published in npj Digital Medicine, analyzed sensor data on sleep, activity and heart rate from over 5,600 individuals.

Testing shortages, long waits for results, and an over-taxed health care system have made headlines throughout the COVID-19 pandemic. These issues can be further exacerbated in small or rural communities in the US and globally. Additionally, respiratory symptoms of COVID-19 such as fever and cough are also associated with the flu, which complicates non-lab diagnoses during certain seasons.

An artificial intelligence (AI) model trained using sequential health information derived from electronic health records (EHR) identified a subset of individuals with a 25-fold risk of developing pancreatic cancer within three to 36 months, according to results presented at the AACR Annual Meeting 2022, held April 8-13.

A new artificial intelligence-based approach can predict, significantly more accurately than a doctor, if and when a patient could die of cardiac arrest. The technology, built on raw images of patient's diseased hearts and patient backgrounds, stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine’s deadliest and most puzzling conditions.