AI at the Frontline: Unmasking Diagnostic Accuracy in Primary Care Triage — A Systematic Review

Abstract

eview question / Objective The question posed for systematic review is: What is the diagnostic accuracy of AI-driven triage systems in primary care settings?Population: Patients undergoing triage in primary care settings are experiencing patient overflow.Intervention: AI-based triage systems used for symptom assessment and patient prioritization.Comparison: Usual care triage, performed by healthcare professionals (physicians, nurses, standard triage protocols).Outcome: To know diagnostic accuracy, Triage efficiency, patient waiting time, provider workload, and patient safety outcomes.Rationale Primary health care systems are increasingly overwhelmed by patient overflow, staff shortages, and rising healthcare costs.Accurate triage is crucial to ensuring patient safety and improving healthcare delivery.Artificial intelligence (AI) technologies offer the potential to enhance triage accuracy, but concerns exist regarding their diagnostic reliability compared to traditional human-led triage methods.This systematic review aims to critically evaluate the diagnostic accuracy of AI-based triage systems in primary care settings to inform clinical practice and guide future AI implementation in frontline care.Condition being studied This systematic review focuses on the triage process in primary care settings, particularly for patients presenting with acute conditions such as respiratory symptoms, fever, or other urgent complaints.The study evaluates the diagnostic accuracy of AI-based triage systems in correctly prioritizing and identifying patients who require urgent care versus those with non-urgent health issues.

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