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

Abstract

OBJECTIVE: This umbrella review of reviews aims to synthesise current evidence on AI's 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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