{"doi":"10.3390/make8040093","title":"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","abstract":"<jats:p>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 &lt; 0.001), accuracy (ES = 3.19, p &lt; 0.001), and NPV (ES = 13.84, p &lt; 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.</jats:p>","journal":"Machine Learning and Knowledge Extraction","year":2026,"id":626794,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":355533,"name":"Antonio Pinto","orcid":"0000-0002-0665-9872","position":1,"is_corresponding":false},{"id":1598837,"name":"Claudia Cozzolino","orcid":"0000-0001-7628-9515","position":2,"is_corresponding":false},{"id":1621552,"name":"Andrea Cozza","orcid":"0000-0001-9156-9312","position":3,"is_corresponding":false},{"id":170078,"name":"Giovanni Rezza","orcid":null,"position":4,"is_corresponding":false},{"id":1609228,"name":"Carlo Signorelli","orcid":"0000-0001-9710-4962","position":5,"is_corresponding":false},{"id":1621553,"name":"Vincenzo Baldo","orcid":"0000-0001-6012-9453","position":6,"is_corresponding":false},{"id":1594054,"name":"Vincenza Gianfredi","orcid":"0000-0003-3848-981X","position":7,"is_corresponding":false},{"id":1594052,"name":"Flavia Pennisi","orcid":"0009-0001-9185-9747","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"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","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 &lt; 0.001), accuracy (ES = 3.19, p &lt; 0.001), and NPV (ES = 13.84, p &lt; 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.","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W7151542179","authors":[],"funders":[],"total_grants":0,"fwci":4.7824,"citation_percentile":0.93607748,"influential_citations":0,"citation_trend":[{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2504-4990/8/4/93/pdf?version=1775556091","host_type":"journal"},{"url":"https://www.mdpi.com/2504-4990/8/4/93/pdf?version=1775556091","host_type":"publisher"},{"url":"https://doi.org/10.3390/make8040093","host_type":"journal"},{"url":"https://doaj.org/article/3e0d65f8b1ac45178e39b84a691f38b8","host_type":"repository"},{"url":"https://hdl.handle.net/11577/3589501","host_type":"repository"}],"fields_of_study":["Mosquito-borne diseases and control","Artificial Intelligence in Healthcare and Education","COVID-19 epidemiological studies"],"mesh_terms":[],"keywords":["Dengue fever","Predictive value","Systematic review","Dengue virus","Triage","Meta-analysis"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T15:15:48.673500Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}