An “AI misdiagnosis” situation is not limited to a computer making a decision on its own. In real Massachusetts medical settings, automated tools are often part of a larger workflow that includes clinician judgment, ordering and reading tests, reviewing imaging, and communicating results. The problem can occur when a tool’s recommendation, prediction, or documentation assistance is treated as more reliable than it should be, when risk signals are not escalated, or when objective findings are overlooked.
In other cases, automation may not directly “cause” the error, but it can still influence the chain of events. For example, a system may route a patient to the wrong triage pathway, highlight one possibility while downplaying others, or generate documentation that shapes what subsequent clinicians believe they are seeing. When the final diagnosis arrives late, the harm is often tied to that earlier decision-making process—what was known, what was missed, and what should have happened next.
Massachusetts patients may experience these issues across different types of care, including hospital emergency departments, urgent care settings, imaging centers, specialty clinics, and laboratory workflows. The setting can matter because each one has its own communication habits, documentation systems, and escalation practices. A strong legal approach looks closely at how your care actually moved from appointment to appointment and from test to test.


