Dr Gillie Gabay

Safe AI in healthcare requires strict governance, bias testing, and transparency before and after deployment. Systems must ensure diverse data, subgroup accuracy, explainability, and human oversight, while continuously auditing performance. Strong policies, accountability, and clinician control are essential to prevent harm, ensure fairness, and build trust in AI-driven care.

AI in healthcare can reproduce biases, leading to unequal outcomes across gender, ethnicity, and socioeconomic groups. Addressing this requires diverse data, transparency, auditing, and strong governance to ensure fair and safe care.

In 2026, healthcare policy moved from voluntary guidance to strict enforcement, focusing on price transparency, AI safety, and patient rights. Providers must publish clear pricing, disclose AI use, ensure explainability, and comply with data residency rules. New reimbursement codes for digital therapeutics and remote monitoring create revenue opportunities, while shared liability and continuous oversight make compliance a core strategic priority.