This systematic review is the first to investigate ChatGPT’s applications in pediatric healthcare systems by assessing its accuracy and readability, with a focus on its impact across key areas such as clinical decision-making, clinical documentation, patient education, and training. The primary question guiding this review is: How does ChatGPT impact pediatric healthcare systems, for example, in terms of improving patient education, enhancing providers’ efficiency, and assisting with clinical decision-making? A systematic review was conducted using PubMed, EMBASE, and Web of Science (February 16, 2025). Inclusion criteria encompassed peer-reviewed studies evaluating ChatGPT in pediatric healthcare (ages 0–18 and guardians). Of 475 screened articles, 58 met eligibility criteria. Two independent reviewers extracted data on study characteristics, intervention types, outcomes, and results. ChatGPT’s primary applications were patient education (n = 38), clinical decision-making (n = 12), and clinical documentation (n = 5). Accuracy was highest in patient education, where it generated educational materials and answered FAQs, though readability was often at a high school level, necessitating adaptation. Clinical documentation benefits included improved efficiency in drafting notes and discharge instructions. However, clinical decision-making and training (n = 3) showed mixed accuracy, particularly in management recommendations and patient care plans. Conclusion: ChatGPT demonstrates potential in enhancing physician efficiency and tailoring patient education in pediatric healthcare. However, most studies relied on observational designs, with only one quasi-experimental study. Further experimental research is required to evaluate AI’s impact on pediatric care system effectiveness and patient outcomes.<Table Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry nameend="c2" namest="c1"> What is Known: • AI has been increasingly integrated into pediatric healthcare, particularly in imaging, diagnostics, and decision support. • Prior studies have explored LLMs like ChatGPT in medical education and training. </entry> </row> <row> <entry nameend="c2" namest="c1"> What is New: • This is the first systematic review assessing ChatGPT’s broader applications in pediatric healthcare, including decision-making, patient education, clinical documentation, and training. • ChatGPT shows high accuracy in patient education and documentation but variable performance in decision-making and training, emphasizing the need for medical supervision. </entry> </row> </tbody> </tgroup> </Table>

Leveraging ChatGPT to strengthen pediatric healthcare systems: a systematic review

期刊论文
发表日期：

2025年7月12日

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Hugo Douma,
Courtney McNamara,
Maria Bakola,
David Stuckler

摘要

This systematic review is the first to investigate ChatGPT’s applications in pediatric healthcare systems by assessing its accuracy and readability, with a focus on its impact across key areas such as clinical decision-making, clinical documentation, patient education, and training. The primary question guiding this review is: How does ChatGPT impact pediatric healthcare systems, for example, in terms of improving patient education, enhancing providers’ efficiency, and assisting with clinical decision-making? A systematic review was conducted using PubMed, EMBASE, and Web of Science (February 16, 2025). Inclusion criteria encompassed peer-reviewed studies evaluating ChatGPT in pediatric healthcare (ages 0–18 and guardians). Of 475 screened articles, 58 met eligibility criteria. Two independent reviewers extracted data on study characteristics, intervention types, outcomes, and results. ChatGPT’s primary applications were patient education (n = 38), clinical decision-making (n = 12), and clinical documentation (n = 5). Accuracy was highest in patient education, where it generated educational materials and answered FAQs, though readability was often at a high school level, necessitating adaptation. Clinical documentation benefits included improved efficiency in drafting notes and discharge instructions. However, clinical decision-making and training (n = 3) showed mixed accuracy, particularly in management recommendations and patient care plans. Conclusion: ChatGPT demonstrates potential in enhancing physician efficiency and tailoring patient education in pediatric healthcare. However, most studies relied on observational designs, with only one quasi-experimental study. Further experimental research is required to evaluate AI’s impact on pediatric care system effectiveness and patient outcomes.

What is Known:

• AI has been increasingly integrated into pediatric healthcare, particularly in imaging, diagnostics, and decision support.

• Prior studies have explored LLMs like ChatGPT in medical education and training.

What is New:

• This is the first systematic review assessing ChatGPT’s broader applications in pediatric healthcare, including decision-making, patient education, clinical documentation, and training.

• ChatGPT shows high accuracy in patient education and documentation but variable performance in decision-making and training, emphasizing the need for medical supervision.