Large Language Models in Healthcare and Medical Domain: A Review

Analysis

Why This Paper Matters

This survey arrives at a critical juncture where LLMs are being rapidly adopted in healthcare, yet their reliability, safety, and efficacy remain under scrutiny. By systematically cataloging the landscape of LLMs from traditional PLMs to cutting-edge models, the paper provides a much-needed structured overview for practitioners navigating this fast-moving field. The emphasis on clinical language understanding tasks—such as named entity recognition, relation extraction, and question-answering—highlights where LLMs can most immediately augment clinical workflows.

The paper's balanced perspective, acknowledging both the enthusiasm and apprehension surrounding LLMs in healthcare, is particularly valuable. It does not shy away from discussing limitations and challenges, which is essential for responsible deployment. For AI practitioners at Neura Market, this review offers a consolidated reference to understand which models and metrics are most relevant for specific healthcare applications.

Technical Contributions

• Comprehensive taxonomy: Categorizes healthcare LLM applications into clinical language understanding tasks (NER, relation extraction, NLI, multimodal, document classification, QA).
• Model comparison: Provides a side-by-side comparison of state-of-the-art LLMs (e.g., GPT variants, BioBERT, ClinicalBERT) and open-source alternatives, noting their strengths and weaknesses.
• Performance metrics: Lists and explains key evaluation metrics used in biomedical NLP, such as F1-score, accuracy, and domain-specific benchmarks.
• Development trajectory: Traces the evolution from PLMs (e.g., BERT) to modern LLMs, showing how scale and training data have improved performance.

Results

The paper does not present new experimental results but synthesizes findings from the literature. It reports that LLMs can achieve high accuracy on clinical QA and NER tasks, often matching or exceeding earlier PLM-based systems. However, it notes that performance varies significantly across tasks and datasets, and that open-source models sometimes lag behind proprietary ones. The review also highlights that evaluation metrics alone are insufficient to capture clinical safety and reliability.

Significance

This survey is a timely resource for AI practitioners and healthcare stakeholders. By mapping the current capabilities and limitations of LLMs, it helps set realistic expectations and identifies areas requiring further research—such as robustness, bias, and interpretability. The paper's structured approach can inform the design of future LLM-based clinical tools and regulatory frameworks. For Neura Market's audience, it underscores the importance of rigorous evaluation and domain-specific adaptation when deploying LLMs in high-stakes medical environments.