AI Medical Compendium Topic

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Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions.

Annales pharmaceutiques francaises
OBJECTIVES: Clinical pharmacists rely on different scientific references to ensure appropriate, safe, and cost-effective drug use. Tools based on artificial intelligence (AI) such as ChatGPT (Generative Pre-trained Transformer) could offer valuable s...

CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures.

Nature communications
The field of bioimage analysis is currently impacted by a profound transformation, driven by the advancements in imaging technologies and artificial intelligence. The emergence of multi-modal AI systems could allow extracting and utilizing knowledge ...

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Drug and chemical toxicology
Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utiliz...

Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.

European radiology
OBJECTIVES: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, trai...

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

Artificial intelligence in medicine
BACKGROUND: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare...

Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models.

Scientific reports
The analysis of veterinary radiographic imaging data is an essential step in the diagnosis of many thoracic lesions. Given the limited time that physicians can devote to a single patient, it would be valuable to implement an automated system to help ...

A simplified similarity-based approach for drug-drug interaction prediction.

PloS one
Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learni...

Deep learning-based 3D brain multimodal medical image registration.

Medical & biological engineering & computing
Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this pape...

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, tim...

Deep Generative Models in Drug Molecule Generation.

Journal of chemical information and modeling
The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence ...