Quality in Continuing Medical Education: Systematic Review
DOI:
https://doi.org/10.22201/fm.20075057e.2026.60.25757Keywords:
Continuing medical education, quality evaluation, artificial intelligence, blockchain, micro-credentialsAbstract
Introduction: Continuing medical education (CME) represents a fundamental component for the professional development of physicians throughout their careers. Objective: To conduct a systematic literature review to identify key elements (dimensions, technologies, and standards) that serve as a basis for assessing quality in Continuing Medical Education (CME), providing a foundational approach rather than developing a comprehensive framework.
Method: A systematic literature review was conducted in PubMed, Scopus, Web of Science, ERIC, and Google Scholar (searches through December 2025). Inclusion criteria: articles in Spanish, English, or Portuguese (2022– 2025) regarding CME quality assessment, emerging technologies, and continuous improvement frameworks. Exclusion criteria: non-systematic reviews, non-empirical studies, or those outside the medical field. Two independent reviewers screened titles, abstracts, and full texts; RoB 2 was used to assess risk of bias. Thirty-seven studies involving 12,450 participants were included.
Results: Four key dimensions were identified (delivery format, academic quality, impact on individual practice, and team impact). Main outcomes: 25 studies (n = 8,920) focused on traditional assessment (Kirkpatrick model); 18 studies (n = 5,230) focused on technologies (AI, LMS, blockchain). Heterogeneity was high (I² > 75%); risk of bias was moderate-to-high in 60% of the studies due to a lack of blinding.
Conclusions: The integration of technologies improves validation; however, evidence remains limited by inconsistency and imprecision. Limitations include publication bias and high heterogeneity.





(1)_6.png)
1.png)

