Radiology Comic: The Dictation Grade (detail)

Integrating clinical data parameters along with chest X-ray findings, the multimodal artificial intelligence (AI) model outperformed the use of clinical parameters only and chest X-rays only for various conditions ranging from congestive heart failure and chronic kidney disease to hypertension and pneumonia.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

Researchers found that the combination of a retrovesicourethral angle, functional urethral length, and bladder funnel on MRI exams in primiparous women had a 94.7 percent AUC for predicting postpartum stress urinary incontinence (SUI).

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

Is the continued rise of artificial intelligence (AI) driven by legitimate “machine learning,” or do the frequently “suspicious” chest X-rays and questionable detection of subtle findings on head CTs reveal a hype-driven train of new products with an ultimately “planned obsolescence”?

In a multicenter study examining four commercially available artificial intelligence (AI) software products for chest X-rays in over 2,000 patients, researchers found sensitivity rates ranging between 33 to 61 percent for vague airspace disease and 9 to 94 percent for small pneumothorax and pleural effusion.