AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Translational Science, Biomedical

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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...

Digital pathology and artificial intelligence in translational medicine and clinical practice.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision ...

Prediction of clinical trial enrollment rates.

PloS one
Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by ins...

Application of Genomic Data in Translational Medicine During the Big Data Era.

Frontiers in bioscience (Landmark edition)
Advances in gene sequencing technology and decreasing costs have resulted in a proliferation of genomic data as an integral component of big data. The availability of vast amounts of genomic data and more sophisticated genomic analysis techniques has...

What predicts citation counts and translational impact in headache research? A machine learning analysis.

Cephalalgia : an international journal of headache
BACKGROUND: We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

Molecular omics
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequenci...

Artificial intelligence-driven translational medicine: a machine learning framework for predicting disease outcomes and optimizing patient-centric care.

Journal of translational medicine
BACKGROUND: Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient...

Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences.

Clinical and translational science
Artificial intelligence (AI) is making a significant impact across various industries, including healthcare, where it is driving innovation and increasing efficiency. In the fields of Quantitative Clinical Pharmacology (QCP) and Translational Science...

AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference.

Clinical and translational science
Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconference at the...