* If you want to update the article please login/register
The results show three main points: artificial intelligence has significantly negative impacts on occupational productivity and jobs, the heterogeneous effects across occupational groups are significant, and a significant moderation function helps shield against displacement risk.
This week, the use of artificial intelligence in image analysis is a hot topic in the radiology community. Specialists' AI computer vision algorithms heavily rely on large-scale image databases, annotated by specialists. We investigated the learning rate of inexperienced evaluators in terms of the correct identification of pediatric wrist fractures on digital radiographs. In 7,000 radiographs over ten days, students with and without a medical background labeled wrist fractures with bounding boxes. According to specialist review, F1 scores as a measure of detection rate u2014as to rise sharply under specialist scrutiny, but not the Intersection against Union as a measure of precision.
Background Colorectal and gastric cancer are two of the leading causes of cancer-related deaths. We therefore created a digital pathology total solution that combined artificial intelligence classification algorithms and pathology laboratory information for GI endoscopic biopsy specimens to produce a post-analytic daily fast quality control device that was used in clinical practice for a 3-month trial run by four pathologists. Methods and findings The whole slide image classification system was composed of patch-generator, patch-level classifier, and a WSI-level classifier. The WSI classifier categorized histopathologic WSIs of colorectal and gastric endoscopic biopsy specimens, respectively, into three classes in laboratory experiments. Pathologists reviewed nearly 70% of GI endoscopy biopsy slides double-checked by the AI models during the 3-month AI-assisted daily QC trial period, compared to that in the conventional monthly QC.
Objects in patients with ocular surface pain using a random forest artificial intelligence model, Objectives: To investigate various corneal nerve parameters using confocal microscopy, as well as systemic and orthoptic parameters. Methods Two hundred forty eyes of 120 patients with primary ocular surface pain or discomfort were examined as well as a control group of 60 patients with no signs of ocular pain. The eyes were grouped as those with a higher prevalence of symptoms and signs, with similar symptoms and signs, but without symptoms and signs, without signs, symptoms, and signs. The accuracy was both highest for Group 2 and lowest for Group 3 eyes. Conclusions: Multiple corneal nerve parameters, as well as systemic and orthoptic disorders associated with AI, were found in this review, and it can be a helpful tool to determine and compare the various clinical and imaging findings of ocular surface pain.
Polyp detection by colonoscopy is a commonly used technique to prevent colorectal cancer. We propose to use various object detection algorithms for polyp detection in this research. With respect to the weighted boxes fusion ensemble technique, our prediction achieves a mAP of 0. 86, resulting in an increase of 39. 9% relative to the best baseline model and 28. 8%.
On the first visit, the observers read images without AI; on the second visit, the same observers used AI to read the same image. Both the sensitivity for lymph node calcification and sialolith were poor, ranging from 0. 727 to 0. 926, respectively, although the sensitivity for soft tissue calcification of AI was high from 0. 727 to 0. 926, respectively. Although the detection sensitivity of sialolith and lymph node calcification was lower than that of carotid artery calcification, the OMR specialists' total reading time was reduced and the GDs' reading accuracy was improved when using AI, although the detection sensitivity of the calcification was lower than that of carotid artery calcification was increased. The AI used in this report helped to enhance the diagnostic accuracy of the GD group, who were not familiar with the soft tissue calcification technique, but more data sets are required to increase the detection accuracy of the two diseases with low sensitivity to AI.
* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions