The dark side of artificial intelligence in medical education and the healthcare system: challenges and strategies for a balanced approach: a systematic meta-analysis

Abstract

Artificial intelligence (AI) has emerged as a paradigm shift in medical education and healthcare systems. It promises improved diagnostic accuracy, customized therapies, and better learning outcomes. Rapidly increasing incorporation of AI technologies has revealed substantial obstacles and potential negative implications that necessitate further investigation. The objective of this study was to conduct a comprehensive systematic meta-analysis of the challenges, hazards and negative consequences concerning the implementation of AI in medical education and healthcare systems, based on recent literature from 2020-2025. We performed a systematic search of scientific literature across multiple databases (PubMed, Scopus, Web of Science, and Cochrane Library) for peer-reviewed publications that had been published in Q1-Q4 journals between 2020 and 2025. Studies were selected in line with specified inclusion and exclusion criteria, alongside data extraction and quality evaluation conducted independently by multiple reviewers. Initially, 247 articles were found, however only 89 of them met the criteria for final analysis. Among most substantial issues that have been found are algorithmic bias (reported in 76% of studies), data privacy concerns (68%), over-reliance on technology (54%), less human connection (49%), ethical dilemmas (72%), and implementation challenges (83%). Meta-analysis revealed that the reported outcomes were very diverse in different healthcare and educational contexts. AI offers substantial benefits, however, its integration into medical education and healthcare systems presents multifaceted challenges requiring careful consideration. Balanced approach incorporating robust ethical frameworks, bias mitigation strategies, and continuous monitoring is essential for responsible AI implementation.

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