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Introduction The use of oral anticoagulation (OAC) in atrial fibrillation (AF) is often complicated by comorbidities such as renal dysfunction and bleeding risk. We developed and tested CLARA (Clinical Logic and Reasoning Algorithm), an AI-driven clinical decision support tool that uses a WhatsApp chatbot interface with a real-time Excel database ( figure 1).Methods A simulated dataset of 150,000 AF patients was generated, each with full clinical profiles including age, sex, CHA 2DS2-VASc components, HAS-BLED criteria, renal function (eGFR), weight, and previous anticoagulant exposure. A WhatsApp chatbot API was built to interact with healthcare providers, capturing structured inputs and relaying them in real time to a backend Excel-based clinical engine. The system matched responses with validated dosing algorithms (DOAC adjustment criteria) to recommend the precise agent and dose.To evaluate accuracy, CLARA was tested on a real-world dataset of 100 complex AF patients—preselected for challenging clinical profiles including renal impairment (eGFR <60) and high bleeding risk. The system’s OAC recommendations were compared against expert cardiologist consensus.Results CLARA achieved a sensitivity of 96% and a specificity of 94% in prescribing the correct agent and dose of OAC, based on gold-standard clinician judgement. Amongst patients with renal dysfunction (n=41), the algorithm correctly adjusted DOAC dosing in 39/41 cases. In patients with HAS-BLED ≥3 (n=36), CLARA accurately flagged bleeding risk and maintained guideline-appropriate prescribing. No inappropriate full-dose DOAC recommendations were issued in patients meeting criteria for dose reduction. The chatbot interaction averaged under 90 seconds per user.Conclusions/Implications CLARA demonstrates high sensitivity and specificity in providing individualized OAC recommendations in AF, including complex patients with renal impairment and bleeding risk. Its WhatsApp-based interface makes it highly accessible and adaptable in real-time clinical workflows. Further prospective validation is planned, but initial results support its potential as a scalable decision support tool for AF.Abstract 21 Figure 1