Exploring the roles of artificial intelligence in surgical education: A scoping review

Bilgiç, Elif; Gorgy, Andrew; Yang, A.F.; Cwintal, Michelle; Ranjbar, Hamed; Kahla, Kalin; Reddy, D. Shankar; Li, Kexin; Ozturk, Helin; E, Zimmermann; Quaiattini, Andrea; Rahimi, Samira Abbasgholizadeh; Poenaru, Dan; Harley, Jason M.

doi:10.1016/j.amjsurg.2021.11.023

Cited by 18 publications

(4 citation statements)

References 72 publications

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“…Understanding foundational principles of ML will be required to effectively interact with many applications of ML, including diagnostic and therapeutic decision support systems (e.g., disease risk prediction models, treatment recommender systems, etc.). Published ML curricula for medical students ( 29 , 30 ) and medical ( 31 , 32 ) and surgical ( 33 ) subspecialities converge on several key domains such as the “critical appraisal of AI systems” and “ethical and legal implications” ( 34 ). We propose below an ML curriculum for PCCM trainees and stakeholders based on similar domains, but with key adaptations for clinicians caring for critically ill children where appropriate.…”

Section: Closing the Gap: A Proposed ML Curriculum For Pccm Trainees ...mentioning

confidence: 99%

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

et al. 2022

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Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.

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Section: Closing the Gap: A Proposed ML Curriculum For Pccm Trainees ...mentioning

confidence: 99%

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

et al. 2022

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“…The applications of AI in education are wide-ranging, which include the personalization of learning platforms that adjust to specific student needs and the development of smart tutoring systems that provide real-time feedback and support [4]. In addition, AI can also help with administrative activities like scheduling and grading, freeing teachers' time to focus on instruction and student engagement [5]. AI tools can analyze large amounts of data to find patterns and trends in student performance.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Survey Instrument on Knowledge, Attitude, and Practices (KAP) Regarding the Educational Use of ChatGPT among Preservice Teachers in the Philippines

Robledo,

Zara,

Montalbo

et al. 2023

IJIET

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ChatGPT has gained popularity among Philippine educational institutions due to its versatility and usefulness in academic activities. However, educators have raised concerns about the ethical issues, academic dishonesty, and technology dependence associated with its use. This study aimed to develop and validate an instrument called KAP-CQ39 that assesses the knowledge, attitudes, and practices of preservice teachers regarding the use of ChatGPT. The study involved 4 experts, 12 independent evaluators, and 206 preservice teachers. The instrument underwent a series of assessments to establish quality, validity, and reliability, resulting in the final version of KAP-CQ39. Results showed that KAP-CQ39 has acceptable content validity (CVR and CVI values>0.78), face validity (IS>1.5), construct validity (chi-squared value of 897.564, df=577, p<0.001), and internal consistency (overall Cronbach coefficient=0.91). This proved that KAP-CQ39 can be a valuable tool for assessing the impact of ChatGPT on the Philippine educational system and can provide insights for educational policy makers regarding the use of artificial intelligence in education.

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“…The construction of contemporary new engineering requires applied undergraduate institutions to build a curriculum system for cultivating artificial intelligence talents. Yet the ability is often neglected in the teaching process [1]- [2]. This lack of support may lead to the necessity of programming courses for non-computer science majors being questioned.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Teaching Path of Artificial Intelligence Programming Course in the Context of New Engineering Construction

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Under the background of new engineering construction and the requirements of new curriculum reform, the problem of using big data technology to combine with the teaching of the “Artificial Intelligence Programming” course has caused many educational researchers to focus on the problem. In this paper, we propose data mining based on a clustering algorithm to build a teaching management system to optimize the teaching path of the Artificial Intelligence Programming course and build a web-based teaching management system based on clustering analysis and clustering validity index. Then the system structure and database are designed through the teaching system requirements, and the course teaching evaluation index system is built based on the teaching objectives of Artificial Intelligence Programming. Finally, the course’s teaching efficiency is analyzed using the teaching system with 20 weeks of teaching Artificial Intelligence Programming in one semester. The results show the clustering algorithm: the average course teaching efficiency is 39.95%, and the BP neural algorithm: the average course teaching efficiency is 34.71%. Genetic algorithm: the average course teaching efficiency is 33.96%. Based on the clustering algorithm teaching system improves the course teaching efficiency by 39.95% compared with the other two better performances, which is conducive to improving the quality of course teaching and teaching reform. This study provides better information technology support for new engineering construction and engineering teaching optimization and has reference guiding value for course teaching research.

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Exploring the roles of artificial intelligence in surgical education: A scoping review

Cited by 18 publications

References 72 publications

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care

Development and Validation of a Survey Instrument on Knowledge, Attitude, and Practices (KAP) Regarding the Educational Use of ChatGPT among Preservice Teachers in the Philippines

Optimization of Teaching Path of Artificial Intelligence Programming Course in the Context of New Engineering Construction

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