From Algorithm to Bedside: Clinically Actionable Artificial Intelligence for Early Disease Detection Using Multimodal Laboratory Biomarkers and Point-of-Care Diagnostics in Resource-Limited Settings
Keywords:
Artificial Intelligence, Early Diagnosis, Laboratory-Derived Biomarkers, Machine Learning, Point-of-Care Testing, Resource-Constrained Settings, Scoping ReviewAbstract
Artificial intelligence (AI) applied to laboratory-derived biomarkers and point-of-care (POC) diagnostics offers substantial potential for early disease detection, but analytical performance does not necessarily translate into clinical actionability, particularly in resource-constrained settings. This scoping review mapped evidence on AI-enabled early disease detection using laboratory biomarkers, POC diagnostics, and multimodal approaches, with emphasis on validation, clinical utility, affordability, equity, and LMIC transferability. Following Joanna Briggs Institute methodology and PRISMA-ScR guidance, relevant primary studies were identified through structured literature searching and citation screening, extracted using a standardized framework, and appraised using PROBAST+AI and QUADAS-2. Of 151 records identified, 86 studies met the inclusion criteria. Discriminative performance was generally high (AUROC frequently 0.80–0.99), but only 29% reported independent external validation, 16% prospective or field validation, and 12% explainability; 5 studies (6%) provided evidence of practical clinical utility or implementation. Multimodal AI represented 14% of studies, while 9 studies (10%) were judged at high risk of bias. Forty-three studies (50%) had explicit LMIC relevance, with the most translationally mature evidence relying on existing diagnostic infrastructure. Overall, substantial translational gaps remain between algorithmic performance and clinically actionable deployment. The proposed Algorithm-to-Bedside Readiness Framework and Resource-Limited AI Diagnostic Implementation Pathway provide structured approaches for advancing validated, equitable, and resource-appropriate diagnostic AI implementation.
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