The multicenter international study, which enrolled 258 patients across 15 sites in the U.S. and Japan, compared HeartFlow’s automated deep-learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography against the reference standard of intravascular ultrasound.
Due to a shortage of radiologists and other imaging specialists, AI-enabled technologies are of significant interest—and hold particular promise—in medical imaging. In this article, Penny Pinnock of Siemens Financial Services (SFS), discusses the routes to investment available for healthcare organizations looking to acquire efficiency boosting technology while protecting their financial health.
Several alternatives exist to obtain FDA approval of artificial intelligence-enabled medical technology. Determining the best approach for a new device will require an awareness of each pathway’s specific requirements. An appreciation of the unique considerations for artificial intelligence-enabled technologies is another essential component of an efficient and effective application.
Study reveals limitations of large language AI models in medical coding.
Although 61% of decision-makers at healthcare organizations said their firms invested at least $100 million in digital transformation initiatives over the past year, 65% of those respondents said their firms have translated less than half of those investments into tangible business value. These numbers highlight the challenges of digital transformation with data siloes posing the biggest threat to success.
Researchers have developed a new machine-learning model that can precisely make prognosis predictions for patients with osteosarcoma, based on the density of viable tumor cells post-treatment.
“Artificial Intelligence and Medical Products: How CBER, CDER, CDRH, and OCP are Working Together” outlines how FDA’s medical product centers plan to address regulation of AI used in medical products and their development.
Mayo Clinic News Network/ — In a review published in Cancers, the researchers note that this emerging class of AI offers an innovative way to use massive datasets to help discover the complex causes of diseases such as cancer and improve treatment strategies.
While the MedTech industry continues to face significant challenges related to regulatory requirements, cybersecurity issues, recalls and lawsuits, leaders in the industry are navigating and overcoming these areas to push innovation forward like never before.
Traditional screening tests suffer from a range of challenges. From logistical barriers to concerns regarding accuracy and reliability, achieving accurate diagnosis is frequently arduous. Imagine a revolutionary approach where early disease screening becomes as simple as collecting a breath sample. Thanks to cutting-edge sensor technology and advanced artificial intelligence, this vision is now on the brink of realization.
