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Artificial intelligence language models for medical text analysis: A systematic review.

Amir Sorayaie Azar1, Jamshid Bagherzadeh Mohasefi1, Uffe Kock Wiil2

  • 1Department of Computer Engineering, Urmia University, Urmia, Iran.

Artificial Intelligence in Medicine
|May 5, 2026
PubMed
Summary

Advanced AI language models like BERT and GPT show promise in analyzing medical text for better clinical decisions. However, challenges in data and interpretability must be addressed for widespread adoption in healthcare.

Keywords:
Artificial intelligenceLanguage modelsMedical textNatural language processing

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Medical text records are vital for clinical decision-making, diagnosis, prognosis, and treatment.
  • Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) offer powerful tools for medical text analysis.
  • AI language models are increasingly used for analyzing, classifying, and generating medical textual data.

Purpose of the Study:

  • To systematically review the application of AI language models in processing medical text.
  • To assess the progress and challenges of using advanced AI architectures like BERT and GPT in healthcare.
  • To identify future research directions for improving AI in medical text analysis.

Main Methods:

  • Systematic literature review of research articles published between January 2000 and July 2024.
  • Initial search yielded 548 records, with 22 original research articles included after screening.
  • Analysis focused on the application and performance of advanced AI models (BERT, GPT) versus conventional NLP/ML approaches.

Main Results:

  • Advanced AI models (BERT, GPT) significantly outperform conventional NLP/ML methods in medical text processing.
  • Superior results were observed in disease classification, automated clinical documentation, and predictive analytics.
  • Key challenges include limited validated datasets, data preprocessing variability, insufficient external validation, and lack of interpretable AI.

Conclusions:

  • AI language models demonstrate substantial progress and superior performance in medical text analysis.
  • Clinical adoption is hindered by data limitations, validation issues, and the need for interpretable AI frameworks.
  • Future research should focus on hybrid AI systems, explainable AI, and standardized reporting for enhanced reliability and clinical impact.