Pabel

General-purpose large language models are known to generate hallucinated content, with studies reporting hallucination rates of up to 37.5% in medical contexts.  In parallel, clinical studies have reported that approximately 15% of diagnostic decisions were found to be incorrect, most frequently in settings marked by high workload, time pressure, and increasing information complexity. Pabel addresses these challenges by providing source-based answers grounded in clinical guidelines and peer-reviewed scientific literature.  Rather than generating opaque AI output, every response is transparently linked to its underlying evidence, enabling healthcare professionals to efficiently verify information in time-critical clinical situations. Designed for daily clinical practice, education, and quality assurance, Pabel supports clinicians in strengthening diagnostic accuracy and decision confidence, as an assistive tool that complements, not replaces, professional medical judgment.