Privacy and bias concerns and integration challenges are brakes on the pace of health care systems adopting the technology.

Artificial intelligence that detects surgical site infections from patient-submitted photos with 73% accuracy could enhance patient care and also reduce clinician burdens following surgeries.

In the current workflow for lung cancer patients, rapid genetic tests are often performed first. These tests use limited tumor tissue and leave about one in four patients without enough material for ...

A new clinical test may be able to predict which biologic may be the most effective for each individual with rheumatoid ...

SIFT and SURF: A Comparative Analysis of Feature Extraction Methods for Image Matching Published in: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) ...

This AI model from Mayo Clinic detects surgical site infections in wound images, improving early diagnosis and streamlining ...

Rochester, Minn.-based Mayo Clinic researchers have developed an AI tool to accurately identify signs of surgical site infection on postoperative online portal images, according to a study published ...

In modern hospitals, timely and accurate decision-making is essential—especially in radiology, where contrast media ...

In this article, a cross-modality image matching network, which we refer to as CMM-Net, is proposed to realize thermal infrared and visible image matching by learning a modality-invariant feature ...