A framework called TRUECAM, developed by researchers at Hong Kong Polytechnic University, is designed to show when pathology AI predictions are trustworthy, when they are uncertain, and when clinicians should take over. The system targets a key failure mode in medical AI: models that provide confident outputs even when they should not be relied upon. By highlighting uncertainty in slide-level analysis workflows, TRUECAM aims to reduce risk from over-automation in cancer diagnostics. The development aligns with regulatory and clinical expectations that AI systems incorporate human-in-the-loop safeguards rather than operating as black boxes. For diagnostic stakeholders, the work supports a shift toward decision support that is auditable and can be integrated into existing quality-management processes.