Pediatrics

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discov...

Vaccination hesitancy: agreement between WHO and ChatGPT-4.0 or Gemini Advanced.

BACKGROUND: An increasing number of individuals use online Artificial Intelligence (AI) - based chat...

Transforming Cancer Care: The Impact of AI-driven Strategies.

AI is a critical component in healthcare, especially in the application of precision medicine where ...

The role of the microbiota-gut-brain axis and artificial intelligence in cognitive health of pediatric obstructive sleep apnea: A narrative review.

Pediatric obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated w...

Co-creating Humanistic AI AgeTech to Support Dynamic Care Ecosystems: A Preliminary Guiding Model.

As society rapidly digitizes, successful aging necessitates using technology for health and social c...

Deep learning-based design and experimental validation of a medicine-like human antibody library.

Antibody generation requires the use of one or more time-consuming methods, namely animal immunizati...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical con...

Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.

Identification of cancer subtypes is a critical step for developing precision medicine. Most cancer ...

AI-Assisted Application for Pediatric Drug Dosing.

Technology in the medical field is continuously advancing due to its numerous subdomains and the eve...

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review.

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) ...

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models f...

Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development-a systematic review.

During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-car...

[Digital Intelligence Drives the High-Quality Development of the Healthcare Service System: Development Mechanisms and Implementation Pathway].

The rapid development of digital intelligence technologies is providing a powerful boost to the high...

Identifying Primary Proximal Caries Lesions in Pediatric Patients From Bitewing Radiographs Using Artificial Intelligence.

To develop a no-code artificial intelligence (AI) model capable of identifying primary proximal sur...

Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Sev...

Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell.

With rapid development and mature of single-cell measurements, single-cell biology and pathology bec...

Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer.

PURPOSE: Data on end-of-life care (EOLC) quality, assessed through evidence-based quality measures (...

Contribution of machine learning for subspecies identification from Mycobacterium abscessus with MALDI-TOF MS in solid and liquid media.

Mycobacterium abscessus (MABS) displays differential subspecies susceptibility to macrolides. Thus, ...

Overlooked Benefits, Risks, and Assumptions of AI Integration in Pediatric Care.

Overlooked Benefits, Risks, and Assumptions of AI Integration in Pediatric Care-Reply.

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor recept...