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. 

With 91% sensitivity but only 25% specificity, the tool is worthwhile for clinicians who remain wary of frequent false positives that would send patients with no fractures for unneeded imaging.

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.

Suggesting the move will significantly advance radiology’s specialtywide imperative to “ditch the disk,” Montreal-based Intelerad Medical Systems has announced it is acquiring a longtime competitor in the image-exchange space.

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, discusses how artificial intelligence (AI) can help combat clinician burnout.

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains several key artificial intelligence (AI) trends he sees across healthcare.