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The paper introduces ImDrug, a benchmark for evaluating deep imbalanced learning methods in AI-aided drug discovery. ImDrug addresses the prevalent issue of imbalanced datasets in this field, offering 11 datasets, 54 tasks, and 16 baseline algorithms. It features novel evaluation metrics (balanced accuracy and balanced F1) to mitigate biases from imbalanced data splits. The authors conduct extensive experiments across various imbalanced learning settings (classification and regression), highlighting the need for improved algorithms in this crucial area. ImDrug is open-source and provides tools for researchers to customize and expand the benchmark.

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The study introduces ELIXR, a novel multimodal artificial intelligence system for chest X-ray analysis. ELIXR combines large language models (LLMs) and radiology vision encoders, achieving state-of-the-art performance in zero-shot and data-efficient classification, semantic search, visual question answering, and report quality assurance. This approach leverages readily available image-text pairs, reducing reliance on expensive expert-labeled data. The modular design allows for adaptability to various tasks and LLMs, making it a potentially versatile tool for radiology and beyond. Key results demonstrate significant improvements over existing methods, particularly in data efficiency.

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This research paper details the development and validation of a deep learning algorithm for detecting abnormalities in chest X-rays. The algorithm, trained on a massive dataset of 2.3 million X-rays, was rigorously tested against radiologist interpretations on independent datasets. Results demonstrate high accuracy in identifying various abnormalities, rivaling the performance of human radiologists. The study highlights the potential of AI to improve the efficiency and accessibility of chest X-ray interpretation globally, particularly in resource-limited settings. However, limitations regarding dataset bias and inter-reader variability are acknowledged.