Voice-driven clinical decision support for lipid management: integrating trial eligibility assessment with AI-powered guideline synthesis

Background Despite the proven efficacy of lipid-lowering therapy, only 30%–60% of eligible high-risk patients receive appropriate statin therapy. Guideline complexity and limited point-of-care access to trial eligibility criteria contribute to treatment gaps. Objective To develop a clinical decision support tool integrating structured clinical trial data with artificial intelligence for personalised lipid management recommendations. Methods We developed a web-based tool using Python/Streamlit incorporating nine landmark trials. The tool employs voice-driven input via OpenAI Whisper, GPT-4 for natural language processing, quantitative trial matching algorithms and comparative analysis of three international guidelines. Results The tool successfully processes natural language patient descriptions, provides granular trial eligibility assessments with specific inclusion/exclusion criteria analysis and generates evidence-based treatment recommendations with associated Class of Recommendation and Level of Evidence designations. Testing across four simulated clinical scenarios—post-ACS (Acute coronary syndromes) with diabetes, primary prevention with diabetes, statin intolerance with ASCVD (Atherosclerotic cardiovascular disease) and residual hypertriglyceridaemia—demonstrated the tool’s ability to identify differential trial applicability and surface alternative evidence pathways. Conclusions This proof of concept demonstrates a novel approach to clinical decision support through quantitative trial matching and AI-powered guideline synthesis. Formal validation is required before clinical implementation.