Abstract: Recommendations extracted from generative artificial intelligence tools such as large language models via prompt engineering for the design of a system for the automated diagnosis of heart disease are followed through to construct suitable artificial intelligence models for the automated diagnosis of heart disease using clinical measurements. The resulting artificial intelligence models are trained, tested and validated on a clinically validated and publicly accessible heart disease dataset. Observed system performance was reasonable compared to the performance of systems developed by artificial intelligence experts by adopting a custom synthesis approach. The artificial intelligence models could be further refined using inputs such as expert and domain knowledge and ultimately incorporated as an automated heart disease diagnosis module in a comprehensive artificial intelligence-driven healthcare system.
Keywords: Heart Disease, Generative Artificial Intelligence (AI), Large Language Model (LLM), ChatGPT, DeepSeek, Artificial Neural Network (ANN), Deep Learning (DL), TensorFlow, Healthcare System, Disease Diagnosis and Prediction.
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DOI:
10.17148/IJARCCE.2025.14201
