Healthcare

AI can be evaluated as clinician decision support for a narrowly defined imaging task, not as an autonomous diagnosis. It requires representative local data, clinical and regulatory review, and task-specific sensitivity, specificity, and failure analysis before any patient-facing use.

A scheduling pilot can use appointment, staffing, and resource constraints to propose alternatives. Its effect should be measured against current wait time, no-show, and utilization baselines, with operations and clinical owners approving every production rule.

Decision support can surface potential interactions from an approved medication knowledge source, but a pharmacist or clinician must make the final determination. Local formulary coverage, alert quality, integration, privacy, and update procedures need validation first.

OCR and language models can be piloted to extract fields from a representative sample of Persian records. Because handwriting and medical terminology create errors, every critical field needs human verification, privacy controls, and an agreed EMR import workflow.

AI can support risk stratification for a precisely defined population and outcome. It must be prospectively validated for local data, bias, calibration, and clinical workflow, and it should inform rather than replace clinician judgment.