Abstract
Timely and accurate diagnosis and treatment in healthcare settings remain critical healthcare challenges. Digital health technology is playing an increasingly vital role in offering solutions to the challenges facing the sector. Therefore, this study aimed to investigate the clinical validity of the ZaiDocTM AI digital health platform in the outpatient departments of health facilities. A cross-sectional study was applied to investigate clinical validation of the platform in disease conditions present in outpatient departments. About 100 study participants presented with target disease conditions were included. The diagnostic accuracy of the platform was assessed by comparing the suggestions from the ZaiDoc AI Digital Health Platform to standard clinical practice guidelines. All validation metrics were analyzed using SPSS version 25 and Excel. The diagnostic capabilities of the ZaiDocTM Platform were evaluated using 95% confidence intervals (CIs) for all metrics. The sensitivity of the platform was found to be 89.13% (CI = 76.96–95.28%). The specificity of the platform was 96.30% (CI = 87.45–98.98%). The Positive Predictive Value (PPV) of the platform was 95.35% (CI 84.52–98.72%). The Negative Predictive Value (NPV) of the platform was 91.23% (CI = 81.06–96.19%). The platform has the potential to support health care services by providing accurate diagnoses in the outpatient departments of the health facilities. Further full-scale study was essential for a wide range of conditions to enhance the scope of the application.
Keywords: Artificial Intelligence, Clinical Validation, Diagnostic Accuracy, Digital Health, Low-resource Settings, Outpatient Care.