The Role of Natural Language Processing in Graduate Medical Education: A Scoping Review

Abstract

The rapid evolution of artificial intelligence, particularly in the form of natural language processing (NLP) and large language models (LLMs), presents new opportunities to enhance graduate medical education (GME). NLP technologies have the potential to improve residency training programs by automating performance feedback, personalizing learning pathways, and identifying competency gaps. However, the integration of these technologies also raises challenges related to privacy, ethical considerations, and algorithmic bias. This review provides a comprehensive evaluation of the application and impact of NLP in GME. A scoping review of the literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Relevant studies from 2018 to 2024 were identified using databases such as PubMed, Scopus, Web of Science, and Google Scholar. Inclusion criteria focused on peer-reviewed studies evaluating NLP applications in residency training programs across various specialties. Data were extracted from 20 studies, and key themes were synthesized to assess the educational, technological, and ethical implications of NLP in GME. The review identified several key areas where NLP is transforming GME. These include automated performance evaluation systems, sentiment analysis of narrative feedback, personalized learning recommendations, and competency assessment algorithms. NLP technologies demonstrated significant potential in reducing administrative workload, improving assessment accuracy, and enhancing the personalization of residency training. However, studies also highlighted concerns regarding algorithmic biases and the need for transparent, ethical frameworks to ensure fair implementation. The integration of NLP in GME offers significant opportunities to streamline educational processes and enhance trainee development. Automated feedback systems can reduce subjective biases and provide more actionable insights for residents. Additionally, NLP applications can identify early indicators of residents at risk of underperformance and support timely interventions. However, the adoption of these technologies requires careful consideration of ethical and legal implications, particularly around data privacy and fairness. NLP has the potential to revolutionize GME by improving the quality and efficiency of residency training programs. While the technology offers promising benefits, further research is needed to address ethical challenges and ensure responsible implementation. Interdisciplinary collaboration between educators, informaticians, and ethicists will be critical to fully realize the potential of NLP in medical education.

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