Utilising artificial intelligence in developing education of health sciences higher education: An umbrella review of reviews

IF 4.2 2区 医学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES Nurse Education Today Pub Date : 2025-04-01 Epub Date: 2025-01-31 DOI:10.1016/j.nedt.2025.106600
Timo Kovalainen , Sari Pramila-Savukoski , Heli-Maria Kuivila , Jonna Juntunen , Erika Jarva , Matias Rasi , Kristina Mikkonen
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Abstract

Objective

This umbrella review of reviews aims to synthesise current evidence on AIʼs utilisation in developing education within health sciences disciplines.

Design

An umbrella review of reviews, review of reviews, based on Joanna Briggs Institute guidelines.

Data selection

CINAHL, ERIC(ProQuest), PubMed, Scopus, and Medic were systematically searched in December 2023 with no time limit. The inclusion and exclusion criteria were defined according to the PCC framework: Participants(P), Concept(C), and Context (C). Two independent researchers screened 6304 publications, and 201 reviews were selected in the full-text phase.

Data extraction

All the reviews that met inclusion criteria were included in the analysis. The reference lists of included reviews were also searched. Included reviews were quality appraised. The results were analysed with narrative synthesis.

Results of data synthesis

Seven reviews published between 2019 and 2023 were selected for analysis. Five key domains were identified: robotics, machine learning and deep learning, big data, immersive technologies, and natural language processing. Robotics enhances practical medical, dental and nursing education training. Machine learning personalises learning experiences and improves diagnostic skills. Immersive technologies provide interactive simulations for practical training.

Conclusion

This umbrella review of reviews highlights the potential of AI in health sciences education and the need for continued investment in AI technologies and ethical frameworks to ensure effective and equitable integration into educational practices.
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利用人工智能发展健康科学高等教育教育:综述综述
目的:本综述旨在综合目前关于人工智能在发展卫生科学学科教育中的应用的证据。根据乔安娜布里格斯研究所的指导方针,对评论进行总括性的审查,对评论进行审查。数据选择于2023年12月系统检索cinahl、ERIC(ProQuest)、PubMed、Scopus和Medic,无时间限制。根据PCC框架定义纳入和排除标准:参与者(P)、概念(C)和背景(C)。两名独立研究人员筛选了6304篇出版物,在全文阶段选择了201篇综述。数据提取所有符合纳入标准的综述都被纳入分析。还检索了纳入综述的参考文献列表。纳入的评论进行了质量评价。用叙事综合的方法对结果进行分析。选择2019年至2023年间发表的7篇综述进行分析。确定了五个关键领域:机器人、机器学习和深度学习、大数据、沉浸式技术和自然语言处理。机器人技术增强了实用的医学、牙科和护理教育培训。机器学习使学习体验个性化,并提高诊断技能。沉浸式技术为实际训练提供交互式模拟。本综述强调了人工智能在卫生科学教育中的潜力,以及继续投资于人工智能技术和伦理框架的必要性,以确保有效和公平地融入教育实践。
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来源期刊
Nurse Education Today
Nurse Education Today 医学-护理
CiteScore
6.90
自引率
12.80%
发文量
349
审稿时长
58 days
期刊介绍: Nurse Education Today is the leading international journal providing a forum for the publication of high quality original research, review and debate in the discussion of nursing, midwifery and interprofessional health care education, publishing papers which contribute to the advancement of educational theory and pedagogy that support the evidence-based practice for educationalists worldwide. The journal stimulates and values critical scholarly debate on issues that have strategic relevance for leaders of health care education. The journal publishes the highest quality scholarly contributions reflecting the diversity of people, health and education systems worldwide, by publishing research that employs rigorous methodology as well as by publishing papers that highlight the theoretical underpinnings of education and systems globally. The journal will publish papers that show depth, rigour, originality and high standards of presentation, in particular, work that is original, analytical and constructively critical of both previous work and current initiatives. Authors are invited to submit original research, systematic and scholarly reviews, and critical papers which will stimulate debate on research, policy, theory or philosophy of nursing and related health care education, and which will meet and develop the journal''s high academic and ethical standards.
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