Comparative Analysis of Large Language Models in Healthcare

• URL: http://arxiv.org/abs/2604.10316v1
• Date: Sat, 11 Apr 2026 18:47:54 GMT
• Title: Comparative Analysis of Large Language Models in Healthcare
• Abstract summary: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare.<n>Their deployment in high-stakes clinical environments raises critical concerns regarding accuracy, reliability, and patient safety.<n>This study addresses the need for a standardized comparative evaluation of LLMs in medical settings.
• Score: 1.9704270315085601
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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