Semantic Cardio Reports Generation Using Generative AI

Abstract

Cardiovascular diseases (CVDs) have been seen to be the leading cause of death in the world, and the clinical records have been a time-consuming and inaccurate procedure. Integration of structured patient data and generative artificial intelligence (AI) offers a unique opportunity, never witnessed before, to automate the process of producing semantically consistent, contextually rich, and diagnostically accurate cardiology reports. This paper presents a new generative architecture, Semantic Cardio Report Generation Using Generative AI, that uses a smaller-sized T5-Transformer model fine-tuned to generate clinical narratives in structured tabular data in a linguistically and semantically interpretable form.
The suggested framework combines the tabular-to-text conversion, feature mapping, and sequence-to-sequence (Seq2Seq) text generation in order to create patient-specific diagnostic summaries. The trained model of 70,000 structured cardiovascular records has high linguistic and semantic alignment with a BLEU score of 0.5606, ROUGE-L score of 0.8806, and BERTScore (F1) score of 0.9768. These findings demonstrate that complete factual accuracy was maintained, and consistent and medically meaningful narratives were generated using the model, which met the standards of cardiology reporting.
The results support the hypothesis that fine-tuned generative models could be useful as reliable clinical documentation assistants or to bridge the gap between numerical diagnostics and text interpretation. This publication adds a step toward a semantically aware, explainable, and ethically implementable AI system in the domain of clinical cardiology, improving interpretability, lessening the number of clinical interactions, and introducing the digital revolution of cardiovascular care.