arXiv:2606.00031v1 Announce Type: new Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment. While traditional machine learning models perform well on structured clinical data, large language models (LLMs) present new possibilities to interpret medical information expressed in natural language. In this work, we develop a hybrid framework that bridges structured clinical data and natural-language representations for CAD prediction. Using a publicly availabl
The increased computational power and architectural advancements in LLMs, alongside growing availability of structured clinical data, enable novel applications in complex medical prediction tasks.
This work demonstrates how LLMs can enhance predictive accuracy and interpretability in critical medical fields like cardiovascular health, potentially improving early diagnosis and risk management.
The integration of LLMs with structured clinical data allows for a more holistic interpretation of patient information, moving beyond traditional statistical models in medical risk assessment.
- · Healthcare AI developers
- · Medical research institutions
- · Patients with cardiovascular disease
- · Diagnostic imaging and data companies
- · Traditional statistical model developers
- · Legacy diagnostic systems
- · Healthcare systems slow to adopt AI
Improved early detection rates for cardiovascular disease via advanced AI models.
Reduced healthcare costs due to preventative interventions enabled by better risk prediction.
LLMs become standard tools in clinical decision support systems, requiring new regulatory frameworks and physician training.
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