In everyday language, people use the term “AI misdiagnosis” to describe cases where a wrong or delayed diagnosis appears connected to automated systems used during care. In Maryland hospitals, outpatient clinics, imaging centers, and lab settings, clinicians may rely on tools that assist with risk scoring, document drafting, triage routing, radiology interpretation support, or lab result workflows. Even when the clinician makes the final decision, automation can still influence what gets ordered, what gets flagged, and what gets treated as urgent.
It’s important to understand that an AI-related issue is usually not treated as a standalone “software defect” claim. Instead, the case typically examines whether the care team met the expected standard of medical judgment for the patient’s situation. If the system suggested a likely condition, the legal question becomes whether clinicians appropriately verified that suggestion, considered alternatives, ordered confirmatory testing, and acted promptly when objective findings did not match the working diagnosis.
Delayed diagnoses can be just as harmful as incorrect ones. In Maryland, where patients may seek care across emergency departments, urgent care, primary care practices, and specialist referrals, diagnostic delays sometimes occur at the handoff points: the moment results are received, the moment abnormal findings are supposed to trigger follow-up, or the moment a new symptom is attributed to the wrong cause.


