Artificial Intelligence Medical Compendium

OBJECTIVES: Polypharmacy rates continue to rise which can lead to increased risk of adverse drug events. Deprescribing is essential but time-consuming, requiring extensive clinical note and chart review. We developed RxReduce, a large language model ... read more 

BACKGROUND: Machine learning (ML) methods are increasingly used for health-related prediction. This study compared logistic regression with five ML algorithms for predicting reported antibiotic treatment following recent fever/cough among children un... read more 

Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with structural brain alterations detectable by structural magnetic resonance imaging (sMRI). Accurate classification of normal control (NC), mild cognitive impairment (M... read more 

In this work, a deep learning-based multimodal framework for evaluating public engagement with public art is introduced by combining computer vision and behavioral analysis. Existing multimodal models have primarily been applied in domains such as ed... read more 

Machine and deep learning algorithms have been proven their efficiency in the medical domain. This is particularly noticeable in medical imaging, where these algorithms have demonstrated exceptional capabilities, often surpassing human experts in det... read more 

Low agricultural productivity is significantly influenced by plant diseases. Most farmers find it difficult to accurately recognize and effectively control plant diseases. Early disease detection is essential to minimizing future losses. This paper p... read more 

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Objectives Increasing manuscript volume has strained peer review, prompting interest in artificial intelligence (AI) as a supportive tool. We evaluated DeepSeek R1 as a structured peer reviewer of neurosurgical case-based manuscripts and compared its... read more 

INTRODUCTION: Health systems are expanding access to artificial intelligence (AI), yet frontline integration into teaching, clinical workflows, and research practice remains uneven. We frame this gap as the "AI embedding deficit," reflecting limited ... read more 

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