Enhancing AI Readiness in Pediatric Surgery: Impact of a Targeted Workshop on Knowledge and Competencies

 SFX Search Permissions and Reprints Abstract Introduction

Despite an awareness of the transformative potential of artificial intelligence (AI) in health care, its development in pediatric surgery seems slow. One major reason may be a lack of formal AI training. This study assesses the basic AI knowledge and the effectiveness of AI workshops (AI-WS).

Materials and Methods

Four AI-WS were held at the International Academy of Pediatric Surgery 2024. Topics included AI principles, real-time algorithm training, and potential AI applications in pediatric surgery. Self-developed surveys consisting of eight pre-WS and nine post-WS questions were conducted, focusing on participants' AI competencies, usage, educational needs, barriers, and future perspectives.

Results

Out of 57 pediatric surgeons, 53 completed both surveys. None had formal AI training. Although 90% were familiar with AI in diagnostic imaging, most had only basic knowledge of AI technology. After the workshop, participants reported a significant increase in the general understanding of AI/machine learning (ML) (p < 0.001). 96% stated that they were better informed about AI/ML applications for clinical practice; 83% expressed interest in further AI training; 91% believed that AI will be more integrated into clinical practice; and over 80% anticipated that AI will improve patient outcomes.

Conclusion

The AI-WS effectively enhanced pediatric surgeons' AI knowledge and their readiness to adopt AI technologies. Even though our study is limited by the relatively low sample size and a potential selection bias, our results still highlight the importance of targeted education in preparing health care professionals for AI integration. The long-term sustainability of knowledge gains, however, has to be examined in further studies.

Keywords artificial intelligence - machine learning - competencies - educational needs - pediatric surgery

*Holger Till and Hesham Elsayed contributed equally to this work.


Publication History

Received: 09 February 2025

Accepted: 06 July 2025

Accepted Manuscript online:
08 July 2025

Article published online:
24 July 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Comments (0)

No login
gif