Recent advances in artificial intelligence have led to speculation that artificial intelligence will one day replace radiologists? Researchers have developed deep learning neural networks that identify signs in radiographic images, such as fractures and potentially cancerous lesions, and in some cases are more reliable than ordinary radiologists. In general, however, the best systems are currently comparable to people's performance and are used only in research environments.
Having said that, deep learning is rapidly evolving, and this technology is much better than previous medical image analysis methods. This may indicate that artificial intelligence plays an important role in the radiology society in the future. Radiology practices will undoubtedly benefit from systems that can quickly read and interpret multiple images, as the number of images has increased much faster than the number of radiologists over the past decade. Therefore, any solution that can reduce manpower, reduce costs, and improve diagnostic accuracy will benefit both patients and patients.
What does this mean for a radiologist? It is alleged that some medical students decided not to study radiology because they were worried that the job would be eliminated. However, we are convinced that the vast majority of radiologists will continue to have jobs in the coming decades - what artificial intelligence will do is to change and improve the job. We believe that there are several reasons why radiologists will not disappear from the workforce, and several of these factors can hinder large-scale automation of other jobs threatened by artificial intelligence.
First, the radiologist's job is not just reading and interpreting images. Like other artificial intelligence systems, radiological artificial intelligence systems perform a single task (weak artificial intelligence). The deep learning model is trained for specific image recognition tasks, such as identifying nodules on the chest CT or bleeding on the MRI of the brain. But to fully identify all possible outcomes in medical imaging, thousands of specific inspection tasks are bound to be performed, and today artificial intelligence can only perform a few tasks. In addition, image interpretation is only one of the tasks performed by radiologists. They also consult other doctors to discuss diagnosis and treatment, including treatment of the disease (such as providing local ablation therapy), performing image-based medical interventions (interventional radiology), defining the technical parameters of the imaging examination to be performed (for the patient's condition), The findings of the images are linked to other medical records and test results, and the surgery and results are discussed with the patient, as well as many other activities. Even if artificial intelligence replaces doctors to interpret images, most radiologists can shift their focus to other necessary activities.
Secondly, the clinical process based on artificial intelligence imaging work is still a long way from being ready for daily use. A survey by Dreyer and the American Academy of Radiology (ACR) Data Science Institute found that different imaging technology vendors and deep learning algorithms focus on different aspects of the applications they face. Even in the FDA-approved deep learning-based nodule detection system, there are different focuses: the likelihood of a lesion, the likelihood of cancer, the characteristics of a nodule, or its location. These unique focuses will make it difficult for hospitals to embed deep learning systems into current clinical practice. As a result, ACR began defining inputs and outputs for deep learning software vendors. The FDA requires that manufacturers verify the validity and value of algorithms before and after they put them into the market. ACR provides a methodology for this. At the same time, ACR has made a clear definition of the clinical process, imaging requirements and output interpretation in an effort to organize and generalize the use of the clinical process, in line with current and future clinical practice. Of course, it takes many years to organize and generalize the use of the situation, which further expands the role of radiologists in the world of artificial intelligence.
Third, deep learning algorithms for image recognition must be trained with "marked data." In the field of radiology, this means that doctors have to diagnose images of patients with cancer, fractures or other signs. In other types of image recognition that have been successful in deep learning, the algorithm has trained millions of labeled images, but there is no centralized repository of radiographic images because they belong to manufacturers, hospitals and doctors, imaging centers, and patients. Having, collecting and tagging them is challenging and time consuming.
Finally, just as autonomous vehicles need to change car supervision and insurance, artificial intelligence is also needed to change medical supervision and health insurance in the medical field, based on which automatic image analysis will become popular. For example, if the machine misdiagnoses a cancer case, who is responsible? Doctors, hospitals, imaging technology vendors, or data scientists who develop algorithms? All of these issues need to be addressed, and progress in this area cannot be as fast as deep learning in the lab. Artificial intelligence radiology devices may need to outperform radiologists—not just as good as they can drive the changes required for regulation and reimbursement.
It seems that the next time you do a mammogram or MRI, your image is unlikely to be viewed only by artificial intelligence algorithms. Like lawyers, financial planners, accountants, and other professionals who see some tasks being handled by smart machines, radiologists will find that current work is changing, not being replaced.
Therefore, radiologists need to adopt new skills and workflows. As a blog post says, only radiologists who refuse to use artificial intelligence will be threatened. Combining artificial intelligence with radiology practices can deliver significant results in terms of medical care and productivity. Increased productivity may even mean that radiologists can spend more time discussing diagnostics and treatment options with other doctors. If the expected improvements in deep learning image analysis are achieved, then healthcare organizations, patients, and payers will turn their attention to radiologists who figure out how to work effectively with artificial intelligence.
To Increase Satiety Powder,Griffonia Seed Extract,Natural Griffonia Seed Extract,Griffonia Seed Extract
Xi'an XJeson Biotech Co., Ltd , https://www.xjesonbio.com