The increasing complexity and workload of clinical radiology leads to inevitable oversights and mistakes in their use as diagnostic tools, causing delayed treatments and sometimes life-threatening harm to patients. While large language models (LLMs) have shown remarkable progress in many tasks, their utilities in detecting and correcting errors in radiology reporting are limited. This paper proposes a novel dual-knowledge infusion framework that enhances LLMs' capability for radiology report proofreading through systematic integration of medical expertise. Specifically, the knowledge infusion combines medical knowledge graph distillation (MKGD) with external knowledge retrieval (EXKR), enabling an effective automated approach in tackling mistakes in radiology reporting. By decomposing the complex proofreading task into three specialized stages of detection, localization, and correction, our method mirrors the systematic review process employed by expert radiologists, ensuring both precision and clinical interpretability. To perform a robust, clinically relevant evaluation, a comprehensive benchmark is also proposed using real-world radiology reports with real-world error patterns, including speech recognition confusions, terminology ambiguities, and template-related inconsistencies. Extensive evaluations across multiple LLM architectures demonstrate substantial improvements of our approach: up to 31.56% increase in error detection accuracy and 37.4% reduction in processing time. Human evaluation by radiologists confirms superior clinical relevance and factual consistency compared to existing approaches.

This article proposes an integral${Q}$-learning scheme to study the optimum management strategy of the battery for energy trading in the microgrid. The obtained optimal strategies are managed to minimize the cost of the microgrid, and simultaneously guarantee the better performance of the battery such that service life of battery is extended. First, the microgrid model is constructed, where renewable energy and profiled loads are considered. To satisfy the microgrid demand and the environmental energy consumption, an integral${Q}$-learning scheme will be developed for the input policy of the microgrid energy system such that the model dynamics are avoided. Moreover, the minimized cost and the property of the battery are considered in the performance function. Rather than the traditional${Q}$-learning iterative scheme, this article proposes a self-learning architecture for the integral-${Q}$learning scheme with two neural networks. The first network can learn the optimal${Q}$-function. Another network is used to learn the optimal action such that the${Q}$-function is minimized and load demand can be satisfied. The network weights are updated with the gradient method and the stability analysis is presented. Finally, the experiment data is used to verify the proposed integral${Q}$-learning scheme.

Obstructive sleep apnea syndrome (OSAS) is a breathing disorder presenting during sleep. Although polysomnography (PSG) is the gold standard to diagnose OSAS, it is an expensive method that is quite complicated to use. Worse, it takes a long time between testing and getting a diagnosis from PSG. Thus, we have designed an algorithm aimed at diagnosing OSAS in a more efficient manner. First, blood oxygen saturation (SpO2) data are processed to obtain statistical features, which are then trained to establish a classification model based on a support vector machine (SVM) strategy; the resulting SVM model performs the diagnosis of OSAS. Furthermore, in order to allow remote diagnosis, we combine our algorithm with a monitoring system. To achieve this, physiological data are collected from a smart phone and then uploaded to the SVM model in the cloud. Once processed, a diagnosis report is returned to the smart phone. A preliminary evaluation of our algorithm based on real-world data is extremely promising as we find its accuracy, sensitivity, and specificity to be 90.2%, 87.6%, and 94.1%, respectively.