MedAI: A Comprehensive Artificial Intelligence Clinical Decision Support System Integrating Multi-Model Reasoning, Bayesian Diagnosis, and Safety-First Architecture

Abstract

Background: Artificial intelligence embedded in clinical decision support systems within electronic health records has improved diagnostic accuracy, referral pathways, and reduced medical errors. However, existing clinical decision support systems lack comprehensive integration of multiple AI reasoning approaches, real-time data gathering, safety monitoring, and clinical workflow integration. Methodology: We developed a novel clinical decision support system, MedAI, incorporating 27+ clinical specialties with a comprehensive differential diagnosis database, vital sign monitoring via webcam, and voice consultation capability. The system includes: (1) a consultant-grade history-taking agent using large language models; (2) a shadow consultant to mitigate cognitive bias; (3) the Calgary-Cambridge framework; (4) Bayesian clinical reasoning with maximum information-gain optimization; (5) adversarial diagnostic reasoning for cognitive debiasing; (6) comprehensive pharmacological decision support with a 300+ drug database including drug interaction checking; (7) robust safety systems with real-time red flag detection and safety netting; (8) FHIR R4 integration; and (9) the PENTLLM framework utilizing five large language models in parallel with a central NEXUS synthesis engine for the final output. Results: We evaluated the MedAI system using two simulated clinical case scenarios of varying complexity. In the first case, a 33-year-old male presented with a 3-day history of post-prandial epigastric burning pain with prior PPI-responsiveness. The system accurately performed structured history-taking as per Calgary Cambridge Framework, generated clinically appropriate differential diagnoses, flagged red flags, and produced guideline-concordant management plans. Independent evaluation by Grok (xAI) rated the consultation output 9/10, affirming alignment with current NICE, BSG, and ACG clinical guidelines. The second case involved a 33-year-old male with a complex multi-system background presenting with bilateral peripheral neuropathy and cardiorespiratory symptoms whilst on prednisolone with suspected pericarditis. The system accurately characterized both neurological and cardiovascular components, flagged multiple red flags, and generated a guideline-concordant differential diagnosis and management plan. Independent evaluation by Grok (xAI) rated the consultation output as 9.5/10. Conclusion: MedAI demonstrates significant advances in clinical decision support systems. As a unified platform, it supports independent consultation cycle from data gathering to management plan and safety netting. The integration of multi-paradigm AI reasoning with robust safety frameworks provides a rigorous foundation for prospective clinical validation. A full manuscript is in preparation Patent Pending at United States Patent and Trademark Office (USPTO)

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