Radiology ’ s Next Phase: Real-Time Collaboration Leading the Way

The following is a guest article by Vivian Liu, COO at Braid Health One thing in healthcare remains constant: the need for answers. This is why radiology is a pillar of our healthcare system because, without a diagnosis, there is no treatment. In today’s digital age, people want things to be fast, convenient, and reliable. The same is true in healthcare. Patients seek fast, crystal-clear answers to their issues. In the last 5–10 years, substantial innovations in imaging, particularly through deep learning methods of image classification, have emerged. A noteworthy development is the augmentation of radiology with Artificial Intelligence (AI). Machine learning technologies will soon likely automate areas such as image segmentation, lesion detection, measurement, labeling, and comparison with historical images. Although AI has provided an extra perspective, the future of radiology relies on improved collaboration among radiologists, technicians, and healthcare providers. When these healthcare professionals can work closely in the diagnostic process, they can share more expertise, and minimize confusion, errors, and delays. This collaborative effort creates a feedback loop that values every member of the care team, even if they are not physically in the same place but connected through the same communication channel. The result? Better patient experiences and clinical outcomes.  Radiology’s Existing Challenges in Diagnostic Delivery Radiology often becomes a frustrati...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Communication and Patient Experience Health IT Company Healthcare IT Braid Health Diagnostic Delivery Radiology Radiology AI Radiology IT Real-Time Collaboration Real-Time Radiology teleradiology Turnaround Time Source Type: blogs