Comparative Diagnostic Performance of Artificial Intelligence Versus Conventional Approaches for Early Detection of Mosquito-Borne Viral Infections: A Systematic Review and Meta-Analysis, with Evidence Predominantly from Dengue Studies

Flavia Pennisi; Antonio Pinto; Claudia Cozzolino; Andrea Cozza; Giovanni Rezza; Carlo Signorelli; Vincenzo Baldo; Vincenza Gianfredi

doi:10.3390/make8040093

journal article Open Access Apr 07, 2026

Comparative Diagnostic Performance of Artificial Intelligence Versus Conventional Approaches for Early Detection of Mosquito-Borne Viral Infections: A Systematic Review and Meta-Analysis, with Evidence Predominantly from Dengue Studies

Flavia Pennisi

Antonio Pinto

Claudia Cozzolino

Andrea Cozza

Giovanni Rezza Carlo Signorelli

Vincenzo Baldo

Vincenza Gianfredi

Machine Learning and Knowledge Extraction Vol. 8 No. 4 pp. 93 · MDPI AG

View at Publisher Save 10.3390/make8040093

Abstract

Background: Early differentiation of mosquito-borne viral infections from other causes of acute febrile illness remains challenging, particularly in endemic and resource-limited settings. Artificial intelligence (AI) models have been proposed to improve early diagnosis, but their incremental value over conventional approaches is unclear. Methods: We conducted a systematic review and meta-analysis of comparative studies evaluating AI/machine learning models versus conventional approaches (clinical assessment, laboratory-based pathways, or traditional statistical models) for early detection of mosquito-borne viral infections. PubMed, Embase, and Scopus were searched through August 2025. Paired performance metrics were synthesized using fixed- and random-effects models. Outcomes included AUC, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Risk of bias was assessed using PROBAST. Results: Thirteen studies met inclusion criteria. Under random-effects models, AI improved sensitivity (ES = 2.64, p = 0.028), specificity (ES = 5.55, p < 0.001), accuracy (ES = 3.19, p < 0.001), and NPV (ES = 13.84, p < 0.001). No consistent advantage was observed for AUC, and PPV findings were inconsistent. Substantial heterogeneity was present across outcomes (I2 = 100%). Most studies relied on internal validation, and PROBAST identified high risk of bias in the analysis domain in over half. Conclusions: AI-based models may enhance threshold-dependent performance metrics, supporting their use as adjunctive decision-support tools for early triage and case exclusion, while external validation and implementation-focused research remain essential.

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Published: Apr 07, 2026
Vol/Issue: 8(4)
Pages: 93
License: View

Authors

F

Flavia Pennisi

PhD National Program in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy; Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy

A

Antonio Pinto

Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy

C

Claudia Cozzolino

Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Via Loredan 18, 35128 Padua, Italy

A

Andrea Cozza

Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Via Loredan 18, 35128 Padua, Italy

G

Giovanni Rezza

Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy

C

Carlo Signorelli

Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy

V

Vincenzo Baldo

Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Via Loredan 18, 35128 Padua, Italy

V

Vincenza Gianfredi

Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Via Loredan 18, 35128 Padua, Italy

Cite This Article

Flavia Pennisi, Antonio Pinto, Claudia Cozzolino, et al. (2026). Comparative Diagnostic Performance of Artificial Intelligence Versus Conventional Approaches for Early Detection of Mosquito-Borne Viral Infections: A Systematic Review and Meta-Analysis, with Evidence Predominantly from Dengue Studies. Machine Learning and Knowledge Extraction, 8(4), 93. https://doi.org/10.3390/make8040093

Comparative Diagnostic Performance of Artificial Intelligence Versus Conventional Approaches for Early Detection of Mosquito-Borne Viral Infections: A Systematic Review and Meta-Analysis, with Evidence Predominantly from Dengue Studies

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