Latest AI and machine learning research in prescriptions for healthcare professionals.
Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharmacovigilance of pharmaceuticals under development, as well as those already in the market. This review was conducted to understand how generative AI c...
Drug Delivery Systems (DDS) have been developed to address the challenges associated with traditional drug delivery methods. These DDS aim to improve drug administration, enhance patient compliance, reduce side effects, and optimize target therapy. To achieve these goals, it is crucial to design DDS with optimal performance characteristics. The final properties of a DDS are determined by several f...
The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied i...
BACKGROUND: Osteoporosis (OP) is one of the most common diseases in the elderly population. It is mostly treated with medication, but drug research an...
Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-gene targets is imperative for developing effectiv...
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic o...
Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelli...
In recent years, the rapid development of generative AI has given rise to a variety of services such as machine translation, sentence summarization, a...
We are living in an era in which AI technology has become widely available and accessible to many people. The field of drug discovery is no exception,...
Accurate identification and localisation of brain tumours from medical images remain challenging due to tumour variability and structural complexity...
Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing ...
Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Mos...
This study aims to explore the automatic classification method of pneumonia X-ray images based on VGG19 deep convolutional neural network, and evalu...
Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most ex...
Deep learning-based discriminative classifiers, despite their remarkable success, remain vulnerable to adversarial examples that can mislead model p...
In the context of personalized federated learning, existing approaches train a global model to extract transferable representations, based on which ...
Imperfect-recall games, in which players may forget previously acquired information, have found many practical applications, ranging from game abstr...
Drug-target interaction is fundamental in understanding how drugs affect biological systems, and accurately predicting drug-target affinity (DTA) is...
Background: Radiation pneumonitis is a side effect of thoracic radiation therapy. Recently, machine learning models with radiomic features have impr...
Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription ...