AI-Augmented Real-Time Ethical Decision-Support for Incidental Findings in Surgery

TL;DR: Imagine a smart assistant in the operating room that helps surgeons decide what to do when they find something unexpected. The experiment would prototype an AI system that integrates the "traffic-light" ethical framework (Raghavendra Reddy P et al., 2024) and dynamically updates recommendations during surgery based on real-time patient data.

Research Question: Can an AI-based decision-support tool, informed by real-time intraoperative data and ethical frameworks, improve surgeon decision-making and patient outcomes when managing incidental findings?

Hypothesis: Integrating AI with established ethical heuristics (like the "traffic-light" model) will both increase surgeon confidence and yield more ethically and clinically sound decisions when addressing incidental findings.

Experiment Plan: - Develop an AI tool that inputs intraoperative data, patient history, and real-time imaging.

• Encode the traffic-light ethical framework into the tool’s logic.
• Simulate surgical scenarios using anonymized patient records and surgeon participants.
• Compare decision quality, confidence, and ethical alignment between AI-assisted and traditional decisions.
• Measure perioperative outcomes and conduct post-simulation interviews for qualitative feedback.

References:

• 1. Raghavendra Reddy P., Arfath A., K.T. (2024). Navigating Ethical Challenges of Incidental Findings in Abdominal Surgery. Indian Journal Of Applied Research.
• 2. Osman Suliman, Jana Alruhili, Mawadah Bali, Masoudah Alshanqiti, R. Alsaedi, Nesreen Alsaedi, Razan Alsaedi, Fatimah Alharbi, Dana Alraddadi, Sara M. Altom. (2025). Incidental Findings during Abdominal Surgery Clinical Significance and Ethical Consideration: A Systematic Review. Journal of Advances in Medicine and Medical Research.