Comparative Analysis of Large Language Models in Healthcare

2026-04-11

AI Summary

This study addresses the lack of standardized evaluation frameworks for large language models (LLMs) in healthcare, which hinders reliable assessment of their accuracy, reliability, and safety in clinical settings. The authors present the first systematic comparison between general-purpose models—such as Grok, LLaMA, and Gemini—and domain-specific models like ChatDoctor across core medical tasks, including clinical note summarization and medical question answering. Leveraging public benchmarks such as MedMCQA, PubMedQA, and Asclepius, the evaluation incorporates multidimensional metrics encompassing linguistic quality, medical factual accuracy, and contextual reliability. The findings reveal that domain-specialized models excel in medical semantic accuracy, whereas general-purpose models demonstrate superior performance in structured question-answering formats, highlighting complementary strengths that offer empirical guidance for model selection in real-world clinical applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generate, and summarize complex medical text. They offer valuable support to clinicians, researchers, and patients, yet their deployment in high-stakes clinical environments raises critical concerns regarding accuracy, reliability, and patient safety. Despite substantial attention in recent years, standardized benchmarking of LLMs for medical applications has been limited. Objective: This study addresses the need for a standardized comparative evaluation of LLMs in medical settings. Method: We evaluate multiple models, including ChatGPT, LLaMA, Grok, Gemini, and ChatDoctor, on core medical tasks such as patient note summarization and medical question answering, using the open-access datasets, MedMCQA, PubMedQA, and Asclepius, and assess performance through a combination of linguistic and task-specific metrics. Results: The results indicate that domain-specific models, such as ChatDoctor, excel in contextual reliability, producing medically accurate and semantically aligned text, whereas general-purpose models like Grok and LLaMA perform better in structured question-answering tasks, demonstrating higher quantitative accuracy. This highlights the complementary strengths of domain-specific and general-purpose LLMs depending on the medical task. Conclusion: Our findings suggest that LLMs can meaningfully support medical professionals and enhance clinical decision-making; however, their safe and effective deployment requires adherence to ethical standards, contextual accuracy, and human oversight in relevant cases. These results underscore the importance of task-specific evaluation and cautious integration of LLMs into healthcare workflows.

Problem

• Large Language Models
• Healthcare
• Benchmarking
• Medical AI
• Clinical Evaluation

Innovation

• Large Language Models
• Medical Benchmarking
• Domain-specific LLMs
• Clinical Evaluation
• Healthcare AI