Having Tackled Workflows and Image Enhancement, Generative AI Targets Diagnostics

The following is a guest article by Morris Panner, President at Intelerad Medical Systems By 2034, the U.S. could face a shortage of up to 124,000 physicians due, in part, to burnout. In the medical imaging field, specifically, this challenge isn’t exactly new – but the technology that could help remedy the problem is. The usage of this potential solution in the imaging space has been delayed due to the perception it’s a bit of a rule-breaker. A fairly recent arrival to radiology, Generative AI has emerged as a powerful resource, showing promise in a variety of tasks, from supporting streamlined workflows to synthetic image generation. AI, in general, is no stranger to medical imaging. Like a high-achieving older sibling, predictive AI has long been a prized resource, used to support diagnostic capabilities and identify risk factors to help predict a patient’s likelihood of developing certain conditions. Its ability to improve detection rates is legendary in radiology circles. Studies have repeatedly detailed impressive statistics, such as a software program that reduced the miss rate of potentially cancerous lesions at eight health centers by more than half – from 32.4 percent to 15.5 percent. Generative AI, on the other hand, has overcome obstacles to claim its place in the field. It has learned to listen and take direction, slowly gaining the trust of those who watched predictive AI set the bar high, as only a big brother can. Generative AI produces content based...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Ambulatory Clinical Health IT Company Healthcare IT Hospital - Health System Clinical Workflow Generative AI Intelerad Medical Systems Medical Images Morris Panner Physician Burnout Physician Shortage Radiolog Source Type: blogs