Artificial Intelligence in Clinical Diagnosis

Abstract

Artificial intelligence (AI) can identify patterns in medical images, physiological signals and clinical records, offering support for screening, triage and selected diagnostic decisions. Its usefulness depends on the clinical question, patient population, reference standard, workflow and consequences of false results. This narrative review discusses diagnostic applications, study design, performance measures, external validation and ethical oversight. Published results from a prospective trial of an autonomous diabetic retinopathy system illustrate sensitivity, specificity and imageability in a defined primary-care setting. Those metrics cannot be generalized to other diseases or devices. Evidence from AI-assisted mammography shows how a system can be studied in a randomized screening workflow, while regulatory authorization remains tied to a specific intended use. Safe deployment requires data-quality checks, clear clinician and patient communication, referral pathways, monitoring for drift and evaluation of outcomes across patient groups.