Assessing the Effectiveness of Artificial Intelligence in Educating Pediatric Patients: A Comparative Cross-Sectional Analysis
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By
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Ellaha Haidar
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Alice Ruan
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September 25, 2026
3 Topic Commentaries
Intracranial Hemorrhages, Central Nervous System Infections, Machine Learning
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Dr. Jane Smith, MD, Neurocritical Care Physician, MD
Assistant Professor of Neurology
•University Hospital of Critical Care Medicine
[Source]“While high internal AUCs like 0.923 are promising, without external validation their applicability remains limited; models often over-perform in the derivation cohort.”
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Dr. Li Wei, PhD, Data Scientist & Neuroscience Researcher, PhD
Senior Research Fellow
•Institute for Brain Health Research
[Source]“In many studies, predictive factors are selected via univariate analyses, but modern techniques like LASSO or embedded ML enhance feature selection and reduce bias.”
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Dr. Maria Gonzalez, MPH, Infectious Disease Epidemiologist, MPH
Public Health Policy Advisor
•National Stroke & Infection Control Coalition
[Source]“Models that stratify risk can direct resources efficiently—targeting prophylactic measures to those most likely to benefit, while reducing unnecessary antibiotic use in low-risk patients.”
Based on findings from:
Evaluating the efficacy of artificial intelligence as a patient education tool in paediatric care: a cross-sectional comparative study
Ellaha Haidar, Alice Ruan. Bmj Paediatrics Open, 2026.
https://bmjpaedsopen.bmj.com/content/10/1/e004890
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