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

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.