Prof Gillie Gabay

Clinical AI can improve diagnostic speed, efficiency, and patient care, but it must remain subordinate to human clinical judgment. A robust Human-in-the-Loop framework requires physician accountability, patient transparency, data protection, continuous algorithmic auditing, and measures to prevent automation bias and clinician deskilling.

Generative AI performs strongly with structured clinical data but struggles to interpret the informal, culturally diverse language patients use to describe symptoms, creating risks for safety, efficiency, liability, and health equity. Successful deployment requires real-world validation, human oversight, continuous monitoring, and patient-centered interfaces designed around natural communication.

AI offers major opportunities for healthcare but remains vulnerable to serious failures when moved from controlled testing into real clinical settings. Safe integration requires strong clinical leadership, human oversight, rigorous validation, cybersecurity controls, and clear liability frameworks, ensuring that technological innovation never bypasses patient safety or medical governance.