- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
Apple hypes iPhone radiology application
OK radiologists, listen up. And watchyour iPhone screen, too, because itmay soon be filled with an image you’llneed to interpret.
OK radiologists, listen up. And watch your iPhone screen, too, because it may soon be filled with an image you’ll need to interpret. At least that’s the message from Apple, whose latest television ad includes the phrase, “If you want to read an MRI, there is an app for that.”
The application is called OsiriX. It is open-source Macintosh-compatible software for medical imaging data display and manipulation.
An Apple official said OsiriX for iPhone caters to neurosurgeons, cardiologists, and ER physicians as well as radiologists. Even consumers can use an iPhone for fast access to their personal imaging records. Health Insurance Portability and Accountability Act privacy implications have not been fully explored, however.
The only way for us to know if radiologists are buying the concept is to ask you directly. Do you use OsiriX, or some other clinical imaging viewing application, on your iPhone or other smart phone? Share your experience with us by sending an e-mail message to james.brice@cmpmedica.com
Study Reaffirms Low Risk for csPCa with Biopsy Omission After Negative Prostate MRI
December 19th 2024In a new study involving nearly 600 biopsy-naïve men, researchers found that only 4 percent of those with negative prostate MRI had clinically significant prostate cancer after three years of active monitoring.
Study Examines Impact of Deep Learning on Fast MRI Protocols for Knee Pain
December 17th 2024Ten-minute and five-minute knee MRI exams with compressed sequences facilitated by deep learning offered nearly equivalent sensitivity and specificity as an 18-minute conventional MRI knee exam, according to research presented recently at the RSNA conference.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.
