LuminaConsent: AI-driven standardization and quality enhancement of urological informed consent documentation

Objective: Informed consent is the cornerstone of modern medical ethics, but current documentation systems negatively impact patient autonomy and clinical quality due to deficiencies in readability, comprehensibility, and standardization. These is sues hinder patient participation and require innovative solutions. This study introduces the AI-powered LuminaConsent system to address standard deficiencies, comprehensibility issues, and efficiency constraints in urological informed consent documents. Methods: In a three-armed comparative study, LuminaConsent (artificial intelligence), Turkish Urological Surgery Asso ciation standard forms, and expert-developed documents were evaluated in 10 urological procedures. The system is based on the RAG architecture, which uses OpenAI’s GPT-4o-mini model and a special knowledge base consisting of 12 clinical publications. Three independent urology specialists conducted a blind evaluation using a 100-point scale across five areas: scientific content accuracy, patient communication effectiveness, quality of risk-benefit information, perioperative guidance, and legal-ethical compliance. RESULTS: LuminaConsent achieved higher performance with mean scores of 82.33 points (SD±4.2) versus 78.77 points (SD±6.1) for professional society standards and 57.43 points (SD±3.8) for specialist documentation, representing statisti cally significant improvements of 43.3% over specialist practices (p<0.001) and 4.5% over professional society standards (p<0.05). The system demonstrated consistent high-quality output across all procedures while generating comprehensive documentation within 96-180 seconds compared to traditional processes requiring multiple days. Conclusion: LuminaConsent offers a pioneering model for systematic AI integration in clinical practice with its evidence based content generation and bilingual processing capabilities. The findings support the potential to empower patient auton omy, reduce application variations, and improve ethical standards.