Making the Most of Image Analytics in Healthcare

Making the Most of Image Analytics in Healthcare

Healthcare Tech Outlook | Thursday, June 27, 2019

The emergence of AI technologies such as data mining has provided healthcare organizations with new and effective tools to manage medical images.

FREMONT, CA: Medical imaging has changed the landscape of medicine, equipping healthcare providers with robust and practical tools to augment their services. The primary functions of conventional imaging devices once ranged from storage to retrieval. However, the emergence of revolutionary imaging systems such as picture archiving and communication system (PACS) and vendor neutral archive (VNA) have transformed medical technology.

The latest technologies leverage artificial intelligence (AI) to deliver advanced analytics to aid in healthcare operations. Although most imaging systems utilize the Digital Imaging and Communications (DICOM) standard, the adoption of PACS and VNA will require comprehensive infrastructure and the collaboration of various departments regardless of the product.

The PACS and VNA platforms have the capabilities of image storage, export and routing, quick access to images from multiple viewing stations, accommodation of DICOM standards, report generation, user templates, and HL7 support. The growing competition in the market has spurred various organizations to integrate AI capabilities to their imaging platforms.

The AI-powered imaging platforms leverage machine learning, algorithms, and image processing to analyze the trove of images and draw actionable insights. It allows the systems to maintain a high level of accuracy during the operations.

For instance, image-based diagnosis of diabetic retinopathy utilizes AI technology to analyze images of the eye and determine the condition. The procedure is carried out through an IDx-DR device and takes no more than a few minutes. The system provides the test results approximately 20 seconds after capturing the image.

The emergence of AI medical imaging devices has enabled healthcare organizations to enhance patient care and improve productivity. The significant capabilities include abnormality detection through machine learning and image processing, smart dictation via natural language processing, health data mining, image processing and analysis, and high-risk patient detection.

The effective implementation of PACS and VNAs require comprehensive planning, not only in infrastructure but also on the end-user fronts. The robust AI systems will need flexible IT environments, along with storage, processing power, and integration support. Also, many of the imaging systems in the market are at a nascent stage and need further upgrading. However, the technology is evolving, and it will not be long before every healthcare organization adopts AI imaging platforms to enhance their services. 

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