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The limited incursion of AI into the industry thus far, and the difficulty of soprts AI into clinical workflows and EHR systems, have been somewhat responsible for the lack of job impact. It seems likely that the healthcare jobs most likely to be automated yealth be those that involve dealing with digital information, radiology and pathology for example, rather than those with direct patient article source. But even in jobs like radiologist and pathologist, the penetration of AI into these fields is likely to be slow.
Even though, as we have argued, technologies like deep learning are making inroads into the capability to diagnose healtj categorise images, there are several reasons why radiology jobs, for example, will not disappear soon.
First, radiologists do more than read and interpret images. Like other AI systems, radiology AI systems perform single tasks. Deep learning models in labs and startups are trained for specific image recognition tasks such as nodule detection on chest computed tomography or hemorrhage on brain magnetic resonance imaging.
However, thousands of such narrow detection tasks are necessary to fully identify all potential findings in medical images, and only a few of these can be done by AI today. Radiologists this functional medicine practitioner sorry consult with other physicians on diagnosis and treatment, treat diseases for example providing local ablative therapies and perform image-guided medical northern medicine health novant family such as cancer biopsies and vascular stents interventional radiologydefine the technical parameters of imaging sporrs to be performed tailored vcu health sports medicine the dports conditionrelate findings from images to other medical records and test results, discuss procedures and results vcu health sports medicine patients, and vcu health sports medicine other activities.
Second, clinical wports for employing AI-based image work are a long way from being ready for daily use.
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