Timely identification of COVID-19 outpatients who are at risk of hospitalization is critical for preventing clinical deterioration and optimizing healthcare resources. Although machine-learning models have demonstrated high predictive accuracy, their... read more
In the infrared ship detection task under complex environments, issues such as high rates of missed and false detections occur due to noise, occlusion, and the indistinct features of small targets. To address these problems, this paper proposes a shi... read more
In a recent article, Weissman et al.1 examined the extent to which artificial intelligence (AI)-based large language models (LLMs) generate clinical decision support (CDS) outputs that meet the criteria of regulated medical devices1 and called for ne... read more
Drawing on the experiences and lessons learned from researchers based in low- and middle-income countries (LMICs) that leverage generative artificial intelligence (GenAI) technologies to address socio-economic challenges, we showcase the considerable... read more
The data processing demands of the digital era have exposed limitations in conventional memory architectures. Gain cell-embedded dynamic random-access memory based on oxide semiconductors is emerging as a solution, addressing scalability, power effic... read more
Convolutional neural networks (CNNs) provide powerful models of neural sensory encoding, but their complexity makes it difficult to discern computations that support their performance. Here, to address this limitation, we developed a linear-nonlinear... read more
OBJECTIVE: To develop and validate a Fully Automated Stratification System (FASS) integrating serum biomarkers, automated radiomic features, and large language model (LLM)-derived semantic features for prognostic prediction in patients with solitary ... read more
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