In everyday healthcare, technology is rarely used in isolation. In South Carolina hospitals, urgent care centers, imaging facilities, laboratories, and outpatient clinics, clinicians may rely on software-assisted tools to interpret scans, flag risk levels, route patients through triage, generate documentation, or suggest possible conditions. When an automated output is wrong, incomplete, or treated as more certain than it should be, the risk of diagnostic error can increase.
An AI misdiagnosis issue usually involves more than “a machine was wrong.” The legal question is whether the overall diagnostic process met a reasonable standard of care and whether any deviation contributed to harm. That could involve the clinician’s reliance on a tool’s suggestion, failure to verify results, inadequate follow-up of abnormal findings, or breakdowns in how test results were communicated and acted upon.
South Carolina families often experience these problems in familiar settings: missed warning signs during repeat visits, delayed recognition of symptoms that worsen over time, and gaps between imaging, lab work, and clinical review. Even when the final diagnosis is correct later, the injury may have occurred in the period when the care team should have recognized the condition sooner.


