43 Clinical logic and reasoning algorithm (CLARA): an AI-enabled whatsapp chatbot for guiding antiarrhythmic drug therapy in atrial fibrillation

Abstract

<h3>Introduction</h3> Appropriate selection of antiarrhythmic drugs (AADs) in atrial fibrillation (AF) requires consideration of structural heart disease, renal and hepatic function, arrhythmia burden, and drug contraindications. CLARA is an artificial intelligence-powered decision support system that uses a WhatsApp chatbot interface integrated with an Excel-based clinical rules engine to guide AAD selection based on individualised patient profiles. <h3>Methods</h3> A synthetic dataset of 150,000 patients with atrial fibrillation was created with varied clinical variables including left ventricular ejection fraction (LVEF), left atrial (LA) size, renal and liver function, AF type, prior AAD exposure, and comorbidities. CLARA was programmed to deliver AAD recommendations. To assess performance, CLARA was tested against cardiologist consensus recommendations in a cohort of 100 complex AF cases, including patients with left ventricular dysfunction, significant structural heart disease, and multiple prior AAD failures (figure 1). <h3>Results</h3> CLARA achieved 91% concordance with expert cardiologist recommendations in selecting the appropriate class and dose of AAD. In 29 patients with LVEF &lt;40%, CLARA correctly excluded Class IC drugs in all cases and recommended amiodarone or rate control. In 18 patients with significant left atrial enlargement, rhythm control was appropriately deprioritised. For patients with prior AAD intolerance (n=26), CLARA accurately identified contraindicated agents. Median response time was under 2 minutes per case. <h3>Conclusions/Implications</h3> CLARA demonstrates high clinical accuracy and responsiveness in recommending antiarrhythmic therapy in complex AF patients. Its integration of expert-derived algorithms with real-time chatbot interaction offers scalable, structured support for personalising rhythm control strategies. Future work will validate its use in prospective real-world AF populations and explore its integration with electronic health records.

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