Healthy lifestyle behaviours are effective in preventing and treating cardiovascular disease. However, the growing body of scientific literature and the prevalence of conflicting studies make it challenging for healthcare practitioners and patients to stay informed. Large Language Models (LLMs), combined with Retrieval-Augmented Generation (RAG), enable automated claim verification and summarization. We enhanced RAG-LLM with extra modules and evaluated performance. Inclusion-Criteria-based filtering of PubMed papers improved verdict performance. Next, for health claims, PICO-based (Population, Intervention, Comparison, Outcome) paper mapping and summarization improves transparency of evidence used for verdict generation (like ‘Berries reduce blood pressure’). Still, the RAG-LLM models we tested have biases towards positivity (too many foods deemed heart healthy) and neutrality (no clear direction). We discuss mechanisms at play and challenges on the route forward.