Year: 2024
Type: article
Abstract: Introduction: in paediatrics, high-frequency emergency department use is defined as repeated emergency visits for reasons that do not require urgent attention or could be managed at a different level of care. Several factors may be associated with this phenomenon, such as socioeconomic, cultural or psychological factors. Its impact on the health care system is significant. Artificial intelligence (AI) has the potential of reducing high-frequency use. Methodology: we assessed the agreement between the information for 101 diseases common in children provided by Gemini AI, a free and open-access service, and the current scientific evidence. We used the adjusted kappa coefficient in this analysis. Results: the AI provided responses for all of the 101 diseases considered in the analysis. The kappa coefficient was 0.857 (95% CI, 0.002) for the identification of the disease, 0.888 (95% CI, 0.003) for the identification of warning signs, 0.876 (95% CI, 0.005) for establishing the need to visit the emergency department and 0.915 (95% CI, 0.003) for the appropriate recommendation of measures to be taken. Conclusions: the text-based artificial intelligence exhibited substantial agreement with protocols used for identification of diseases based on symptoms, and near-perfect agreement for determining the need to visit the emergency department, identifying warning signs and providing therapeutic recommendations. The level of agreement was higher for common diseases and children aged more than 3 months.
DOI: 10.60147/dce30dee
Authors: Raquel Bernal, Ana Valer, María Celada, Sara Calmarza, Elena Calmarza
Institution: Asociación Española de Pediatría
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4405554144