An AI misdiagnosis case generally involves a medical diagnostic error where automated tools may have influenced how information was interpreted or decisions were made. These tools can include clinical decision support systems, imaging or lab interpretation software, triage or risk scoring platforms, or documentation and workflow assistance that shapes what clinicians see and when they see it. Importantly, the legal issue is not usually whether the technology is “smart” or “bad.” The legal issue is whether the care team and the healthcare organization met the accepted standard of care while using whatever tools were available.
In Connecticut hospitals, outpatient imaging centers, urgent care settings, and lab systems, an AI-related error may show up as an abnormal result being overlooked, a pattern being missed, a recommendation being treated as definitive when it should have been verified, or critical follow-up not happening soon enough. Sometimes the tool’s output is wrong; other times the tool is accurate but the clinical team fails to integrate it correctly with symptoms, history, and objective findings.
Because healthcare is team-based, these cases often involve more than one decision-maker. A clinician might order tests based on risk scoring, a radiology report might be delayed or interpreted incompletely, or follow-up instructions might not reach the right person. When technology is part of the workflow, it can affect documentation and communication, which is why evidence preservation and record review become essential.


