A Strategy for Artificial Intelligence With Clinical Impact—Eyes on the Prize

Nijman, Joppe; Zoodsma, Ruben S.; Koomen, Erik

doi:10.1001/jamapediatrics.2023.6259

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The upskilling and reskilling are a pressing issue, hindering future advances in patient care and improvements in health outcomes. Therefore, a new approach is needed to combine AI and high-quality input data [ 9 ]. This narrative review aims to provide a conceptual introduction to AI and raise awareness of its emerging clinical tools and potential applications in pediatric emergency medicine.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Biomedicines

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The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Di Sarno,

Caroselli,

Tonin

et al. 2024

Biomedicines

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From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit

Schouten,

Kalden,

van Twist

et al. 2024

Intensive Care Med

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Purpose Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benefits is necessary to improve neonatal and pediatric care for critically ill patients. This systematic review seeks to assess the maturity of AI models in neonatal and pediatric intensive care unit (NICU and PICU) treatment, and their risk of bias and objectives. Methods We conducted a systematic search in Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar. Studies using AI models during NICU or PICU stay were eligible for inclusion. Study design, objective, dataset size, level of validation, risk of bias, and technological readiness of the models were extracted. Results Out of the 1257 identified studies 262 were included. The majority of studies was conducted in the NICU (66%) and most had a high risk of bias (77%). An insufficient sample size was the main cause for this high risk of bias. No studies were identified that integrated an AI model in routine clinical practice and the majority of the studies remained in the prototyping and model development phase. Conclusion The majority of AI models remain within the testing and prototyping phase and have a high risk of bias. Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models. Specific guidelines and approaches can help improve clinical outcome with usage of AI. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-024-07629-8.

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A Strategy for Artificial Intelligence With Clinical Impact—Eyes on the Prize

Abstract: This Viewpoint describes a strategy for addressing major challenges in artificial intelligence in pediatrics to maximize clinical impact.

Cited by 2 publications

References 7 publications

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit

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