What failure scenario occurs when a highly probable, benign diagnosis causes the AI to filter out other dangerous symptoms later mentioned by the user?

Diagnostic overshadowing

Diagnostic overshadowing is a specific failure scenario developers must address, occurring when the AI locks onto an initial, highly probable, yet ultimately harmless diagnosis (like simple indigestion). This premature conclusion causes the system to minimize or entirely filter out subsequent, potentially critical symptoms reported by the user, such as chest pain, because those later inputs fall outside the established initial diagnostic pathway. This issue underscores the ongoing engineering challenge of building flexible AI that can revise its primary hypothesis continuously based on all subsequent user input, a capability that distinguishes advanced systems from their simpler predecessors which relied on more static processing paths.