Occupational ApplicationsThe adoption of AI in healthcare depends on calibrated trust-trust that matches the system's reliability and context. This review shows that clinicians value workload reduction, explainability, and alignment with clinical judgment, while patients emphasize transparency, fairness, and human-like interaction. Yet, trust is not automatic: performance gains may fail if AI undermines professional autonomy, and explainability reassures novices more than experts in high-stakes tasks. For occupational applications, AI must be designed to reduce cognitive burden, respect user expertise, and adapt to domain-specific needs. Organizations should invest in usability testing, peer and organizational support, and targeted training to foster informed trust. Regulators should enforce transparency and human oversight standards. Ultimately, calibrated trust, avoiding blind reliance or excessive skepticism, is essential to protect healthcare workers and patients while ensuring AI strengthens decision-making and safety.