An Iterative Systematic Analytical Review of Large Language Models for Medical Applications Using GPT-4, BERT Variants, and Vision Transformers

Abstract

Introduction: The increasing adoption of Large Language Models (LLMs) in healthcare necessitates a comprehensive review of their applications, limitations, and potential. Existing literature lacks a systematic assessment of LLM performance across diverse healthcare tasks and does not adequately address critical aspects such as model-specific optimizations, domain adaptability, and real-world deployment constraints. Objectives : This paper aims to fill the identified gaps by conducting an extensive and structured review of current research on LLM applications in medical reports, diagnostics, and decision-making. It seeks to classify and evaluate studies based on methods used, performance measures, key takeaways, strengths, and limitations. Methods : A PRISMA-based methodology was employed to systematically categorize studies according to their approaches and outcomes. The analysis focused on multiple LLMs, including GPT-3, GPT-4, BERT variants, Med-PaLM, and domain-specific adaptations such as BioGPT and COMCARE. For vision-language transformer-based auto-report generation, PEGASUS and ETB MII were examined. Additionally, the study explored KELLM for causal reasoning with knowledge graphs and OpenMedLM for equitable healthcare solutions. The selected models were evaluated based on key performance metrics such as accuracy, sensitivity, and explainability. Results : The findings indicate that specific LLMs show significant promise in enhancing healthcare applications. Models like Med-PaLM and BioGPT demonstrate improved diagnostic accuracy, while vision-language transformers such as PEGASUS enhance automated medical report generation. The integration of knowledge graphs in KELLM ensures greater interpretability and safety. Open-source models like OpenMedLM contribute to equitable access to AI-driven healthcare solutions. Overall, LLMs can reduce clinician workload, enhance diagnostic precision, and optimize healthcare workflows. Conclusion : This study highlights the transformative potential of LLMs in medicine while also addressing challenges such as ethical considerations, energy efficiency, and scalability. By providing a systematic evaluation, this review paves the way for future advancements in AI-driven healthcare applications, fostering innovation and improved patient care.

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