Magoba Ronald Arnold
Background: Infectious diseases remain a leading cause of morbidity and mortality globally, particularly in low- and middleincome countries where healthcare systems are constrained by limited diagnostic capacity and weak surveillance systems [1]. Artificial intelligence (AI)-enabled clinical decision support systems (CDSS) offer a promising approach to improving early disease detection and outbreak response.
Methods: A mixed-methods cross-sectional study was conducted among 210 healthcare workers across selected Kenyan health facilities. Quantitative data were collected using structured questionnaires and analyzed using logistic regression and chisquare tests. Qualitative data were collected through key informant interviews and analyzed thematically.
Results: AI-assisted diagnosis significantly improved diagnostic accuracy (78% vs. 52%, p < 0.001) and adherence to clinical guidelines (85% vs. 60%, p < 0.01). Healthcare workers reported high usability (80%) and improved confidence (72%). As shown in Table 2 and Table 3, AI integration was associated with better clinical performance outcomes
Conclusion: AI-enabled decision support systems significantly improve infectious disease surveillance and clinical decisionmaking. Scaling up AI integration could strengthen health system performance in Kenya.