Short‐term traffic flow forecasting is a fundamental and challenging task since it is required for the successful deployment of intelligent transportation systems and the traffic flow is dramatically changing through time. This study presents a novel hybrid dual Kalman filter (H‐KF ² ) for accurate and timely short‐term traffic flow forecasting. To achieve this, the H‐KF ² first models the propagation of the discrepancy between the predictions of the traditional Kalman filter and the random walk model. By estimating the a posteriori state of the prediction errors of both models, the calibrated discrepancy is exploited to compensate the preliminary predictions. The H‐KF ² works with competitive time and space to traditional Kalman filter. Four real‐world datasets and various experiments are employed to evaluate the authors’ model. The experimental results demonstrate the H‐KF ² outperforms the state‐of‐the‐art parametric and non‐parametric models.

Learning from AI-generated annotations is wellrecognized as a key advance of deep learning techniques in medical image segmentation. Towards this direction, in this paper, we investigate two questions: (1) how to accurately measure loss value on AI-generated annotations that often contain errors and (2) how to effectively update model’s parameters when the loss value is no longer a correct supervision for medical image segmentation. The main results are that (1) ‘error-tolerant’ loss functions exist and (2) ‘cross-training’, updating the model using data with a small loss of its ‘twin’ model, can tolerate the loss function to some extent. Per the main results, we yet derived a robust training algorithm, called confidence regularized coteaching, that helps deep models to combat annotation errors in medical image segmentation. This algorithm simultaneously trains two ‘twin’ segmentation models and updates model’s parameters by cross-training with disagreement confident data that are predicted differently by the two models, thereby being able to learning from data with annotation errors. The empirical evidence from a publicly available dataset shows that this new algorithm works better on combating annotation errors than existing methods for medical image segmentation, opening the opportunity to use AI-generated annotations to train segmentation model for medical image segmentation.

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs’ significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual’s genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs’ potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients.