Advances in Point-of-Care Infectious Disease Diagnostics: Integration of Technologies, Validation, Artificial Intelligence, and Regulatory Oversight
Bibliographic record
Abstract
Point-of-care (POC) infectious disease diagnostics are reshaping global health by delivering rapid, decentralized, and clinically actionable results that link bedside testing to population-level surveillance. Valued at approximately USD 53 billion in 2024 and projected to nearly double by 2033, the global POC diagnostics market is driven by infectious disease assays and accelerated by innovations in molecular amplification, biosensors, microfluidics, and artificial intelligence (AI). This review integrates current evidence across technological, clinical, regulatory, and public health domains. Immunoassays remain the backbone of volume deployment, while molecular nucleic acid amplification tests (NAATs) and emerging CRISPR-based platforms achieve laboratory-grade sensitivity at the point of care. AI has transitioned from an experimental tool to an embedded analytical layer that enhances image interpretation, multiplex signal deconvolution, and automated quality control. Rigorous validation, including analytical accuracy, clinical performance in intended-use settings, and usability testing under CLIA guidance, remains central to ensuring reliability in decentralized environments. Regulatory frameworks are adapting in parallel: FDA's lifecycle oversight of AI-enabled devices, the European IVDR's expanded evidence requirements, and the WHO Prequalification all emphasize continuous post-market surveillance. From a public health perspective, POC diagnostics have improved early case detection, treatment initiation, and outbreak containment for HIV, tuberculosis, malaria, influenza, RSV, and COVID-19. Yet persistent challenges (including limited harmonization of standards, uneven reimbursement, and scarce real-world data from low- and middle-income countries) continue to constrain equitable adoption. POC infectious disease diagnostics are thus entering a pivotal phase of digitization and regulatory maturity. Addressing remaining gaps in validation, lifecycle monitoring, and implementation equity will determine whether these technologies achieve their full promise as clinical accelerators and as cornerstones of global infectious disease preparedness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".