Sebastian Fox examines production clinical-note failuresAI clinical note generation uses AI to draft or structure medical documentation from clinical information and conversations. that can look acceptable to generic evaluatorsEvaluation is the systematic process of testing and judging an AI system against defined tasks, evidence, and success criteria. while omitting symptoms, adding unsupported diagnoses or medicationsA hallucination is an AI output that presents false, unsupported, or invented information as though it were reliable., or missing a clinician's final decision. These errors show why surface-level accuracy scores are not enough in high-stakes medical workflowsClinical AI applies AI systems to clinical care, documentation and decision support under medical safety and oversight requirements..
Sebastian Fox proposes discovering failure modes from real outputs instead of guessing them in advance. Domain experts should label what went wrong and explain the relevant clinical judgment so evaluation criteria reflect the consequences of each case.
The resulting evaluation loop should retrieve similar cases, current guidance and prior expert corrections for every new output. Because medical standards and model behavior both change, the checks must be continuously recalibratedContinuous AI evaluation repeatedly tests deployed AI against new cases, failures, guidance and behavior changes. rather than frozen into one static rubric.
Watch on YouTube
