AI Medical Compendium Journal:
Clinical and translational science

Showing 1 to 10 of 35 articles

Opportunities and challenges of 5G network technology toward precision medicine.

Clinical and translational science
Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network ...

Artificial intelligence in rare disease diagnosis and treatment.

Clinical and translational science
Artificial intelligence (AI) utilization in health care has grown over the past few years. It also has demonstrated potential in improving the efficiency of diagnosis and treatment. Some types of AI, such as machine learning, allow for the efficient ...

Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.

Clinical and translational science
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-a...

Artificial intelligence in pharmacology research and practice.

Clinical and translational science
In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical prac...

Advancing an agile regulatory ecosystem to respond to the rapid development of innovative technologies.

Clinical and translational science
Technological advancements are dramatically changing the landscape of therapeutic development. The convergence of advances in computing power, analytical methods, artificial intelligence, novel digital health tools, and cloud-based platforms has the ...

Machine learning approach to identify adverse events in scientific biomedical literature.

Clinical and translational science
Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in drug marketing surveillance. This is a very time-consuming and complex task to fulfill the compliance and, most importantly, to ensure patient safety. ...

Computational models in inflammatory bowel disease.

Clinical and translational science
Inflammatory bowel disease (IBD) is a chronic and relapsing disease with multiple underlying influences and notable heterogeneity among its clinical and response-to-treatment phenotypes. There is no cure for IBD, and none of the currently available t...

Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models.

Clinical and translational science
This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We co...

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.

Clinical and translational science
Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical...

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical and translational science
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...