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Objective <p>With the rapid development of big data and artificial intelligence technologies, large language models (LLMs) are increasingly being applied across multiple fields. In the healthcare domain, efficient utilization of extensive patient data and medical records considerably enhances diagnostic accuracy and enables disease-risk prediction. This study aims to establish a research framework for evaluating the application of assessment models in structured and unstructured data, exploring the potential applications of LLMs in healthcare.</p> Methods <p>This study proposes the HLLM-Potential Framework (Healthcare Large Language Model Potential Evaluation Framework), a comprehensive evaluation framework designed to assess the applicability and performance of LLMs in medical data analysis through comparative experiments with traditional models. Publicly available and standardized cardiovascular datasets were adopted, covering both structured and unstructured data. Existing LLMs were utilized through training and task-specific configuration on medical data to perform disease prediction and health-risk assessment. In addition, various LLMs were systematically compared with traditional machine-learning and deep-learning models to quantify the differences in their predictive performance.</p> Results <p>Existing LLMs can process structured and unstructured medical data and use them to predict diseases and evaluate health risks. For structured cardiovascular disease prediction tasks focusing on heart failure, all considered LLMs achieved accuracy and recall rates above 80%. Meanwhile, in unstructured cardiovascular data analysis for ECG image classification, the multimodal LLM Janus Pro 7B attained an overall accuracy rate of 85%.</p> Conclusion <p>Compared with traditional machine learning and deep learning models, the considered LLMs exhibit stronger interactivity and generalization capabilities as well as less dependence on data quality and feature engineering. Overall, LLMs offer notable advantages, namely, high efficiency, high flexibility, and multimodal integration, for use in the healthcare field; thus, LLMs are expected to be widely applied in the medical and healthcare domains.</p>

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Exploring the potential of large language models in healthcare: a focus on cardiovascular disease analysis

  • 期刊论文

  • 发表日期：

    2026年1月31日

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  • Aihua Li,
  • Xinran Bi,
  • Sifan Chen,
  • Juxiang Hu,
  • Yong Shi

摘要

Objective

With the rapid development of big data and artificial intelligence technologies, large language models (LLMs) are increasingly being applied across multiple fields. In the healthcare domain, efficient utilization of extensive patient data and medical records considerably enhances diagnostic accuracy and enables disease-risk prediction. This study aims to establish a research framework for evaluating the application of assessment models in structured and unstructured data, exploring the potential applications of LLMs in healthcare.

Methods

This study proposes the HLLM-Potential Framework (Healthcare Large Language Model Potential Evaluation Framework), a comprehensive evaluation framework designed to assess the applicability and performance of LLMs in medical data analysis through comparative experiments with traditional models. Publicly available and standardized cardiovascular datasets were adopted, covering both structured and unstructured data. Existing LLMs were utilized through training and task-specific configuration on medical data to perform disease prediction and health-risk assessment. In addition, various LLMs were systematically compared with traditional machine-learning and deep-learning models to quantify the differences in their predictive performance.

Results

Existing LLMs can process structured and unstructured medical data and use them to predict diseases and evaluate health risks. For structured cardiovascular disease prediction tasks focusing on heart failure, all considered LLMs achieved accuracy and recall rates above 80%. Meanwhile, in unstructured cardiovascular data analysis for ECG image classification, the multimodal LLM Janus Pro 7B attained an overall accuracy rate of 85%.

Conclusion

Compared with traditional machine learning and deep learning models, the considered LLMs exhibit stronger interactivity and generalization capabilities as well as less dependence on data quality and feature engineering. Overall, LLMs offer notable advantages, namely, high efficiency, high flexibility, and multimodal integration, for use in the healthcare field; thus, LLMs are expected to be widely applied in the medical and healthcare domains.