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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.

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This systematic review explores the application of generative AI, particularly deep generative models (DGMs) and large language models (LLMs), in revolutionizing precision medicine. The authors analyze research from Scopus and PubMed databases, focusing on how generative AI improves synthetic data generation for enhanced accuracy and privacy in clinical informatics, medical imaging, and bioinformatics. The review highlights the successes and limitations of various generative AI techniques in personalized medicine applications, such as drug response prediction and disease diagnosis. It emphasizes the need for further interdisciplinary research to address challenges like data scarcity and model generalizability, ultimately aiming to advance personalized healthcare. A significant finding is the emerging role of LLMs in supporting clinical decision-making, though their limitations are acknowledged.

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This research review explores three key methodological approaches to enhance the use of artificial intelligence (AI) in medical decision-making. Explainable AI focuses on making AI models more transparent and interpretable to build trust. Domain adaptation addresses the challenge of applying AI models trained on one dataset to different datasets. Federated learning enables the training of large-scale AI models without compromising patient data privacy by using distributed collaboration. The paper provides an overview of existing methods, examines their applications in medicine, and discusses challenges and future directions. The authors also analyze current research trends in each area, highlighting strengths and limitations.