New Diagnoses of Juvenile Idiopathic Arthritis Early in the COVID-19 Pandemic: Analysis of a Large United States Commercial Insurance Database.

Background/objective: Little is known about the rates of rheumatic disease diagnosis among children during the COVID-19 pandemic. We examined the impact of the pandemic on the diagnosis of juvenile idiopathic arthritis (JIA) in the United States.

Methods: We performed a historical cohort study using US commercial insurance data (2016-2021) to identify children aged <18 years without prior JIA diagnosis or treatment in the prior ≥12 months.

Reliability of high-quantity human brain organoids for modeling microcephaly, glioma invasion and drug screening.

Brain organoids offer unprecedented insights into brain development and disease modeling and hold promise for drug screening. Significant hindrances, however, are morphological and cellular heterogeneity, inter-organoid size differences, cellular stress, and poor reproducibility. Here, we describe a method that reproducibly generates thousands of organoids across multiple hiPSC lines.

Artificial intelligence in food and nutrition evidence: The challenges and opportunities.

Science-informed decisions are best guided by the objective synthesis of the totality of evidence around a particular question and assessing its trustworthiness through systematic processes. However, there are major barriers and challenges that limit science-informed food and nutrition policy, practice, and guidance. First, insufficient evidence, primarily due to acquisition cost of generating high-quality data, and the complexity of the diet-disease relationship.

Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement.