An AI startup in the neuro-oncology space has received the government’s go-ahead to market software for analyzing certain fast-growing brain tumors on MRI.

If generalizable AI models are to meaningfully contribute to precision cancer care, they’ll need to incorporate not only imaging data but also digitalized clinical notes, biomarker assays and monitor readouts.

A Big Four tech company has launched a platform it hopes will accelerate data interoperability and AI adoption in, specifically, medical imaging.

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, shares some of technology trends observed in radiology PACS and and imaging informatics since 2019.

Validation and testing of all algorithms is needed to eliminate any biases in the data used to train the AI, according to Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America.

Patients with autoimmune hepatitis may be better monitored across disease stages by AI-augmented multiparametric MRI than by liver biopsy, as the imaging has proven less costly and is inherently less risky due to its noninvasiveness. 

A machine learning system has come along that needs no human labeling of data for training yet matches radiologists at classifying diseases on chest X-rays—including some that the model was not specifically taught to detect.

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains the use of artificial intelligence (AI) algorithms to help address health disparities and the rise of healthcare consumerism.