AI in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability.

[BACKGROUND] Artificial intelligence (AI) integrated with point-of-care imaging is a promising approach to expand access in settings with limited specialist availability. However, no systematic review has comprehensively evaluated AI-assisted clinical decision support across multiple point-of-care imaging modalities, assessed explainability implementation, or quantified clinical impact evidence gaps.

[OBJECTIVE] We aim to systematically evaluate and synthesize evidence on AI-based clinical decision support systems using point-of-care imaging.

[METHODS] We searched PubMed, Scopus, IEEE Xplore, and Web of Science (January 2018 to November 2025). We included research studies evaluating AI or machine learning systems applied to point-of-care-capable imaging modalities in clinical settings with clinical decision support outputs. Two reviewers independently screened studies, extracted data across 15 domains, and assessed methodological quality using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). Proposed frameworks were developed to evaluate explainability implementation and clinical impact evidence. Narrative synthesis was performed due to substantial data heterogeneity.

[RESULTS] Of 2113 records identified, 20 studies met inclusion criteria, encompassing approximately 78,000 patients across 15 countries. Studies evaluated tuberculosis (n=5), breast cancer (n=3), deep vein thrombosis (DVT) (n=2), and 9 other conditions using ultrasound (7/20, 35%), chest x-ray (5/20, 25%), photography-based and colposcopic imaging (3/20, 15%), fundus photography (2/20, 10%), microscopy (2/20, 10%), and dermoscopy (1/20, 5%). Median sensitivity was 93.6% (IQR 87%-98%), and median specificity was 90.6% (IQR 74.5%-96.7%). Task-shifting was demonstrated in 65% (13/20) of studies, with nonspecialists achieving specialist-level performance after a median of 1 hour of training (range 30 minutes to 6 months; n=6 studies reporting specific durations). The explainable artificial intelligence (XAI) implementation cascade revealed critical gaps: 75% (15/20) of studies did not mention explainability, 10% (2/20) provided explanations to users, and none evaluated whether clinicians understood explanations or whether XAI influenced decisions. The clinical impact pyramid showed 15% (3/20) of studies reported technical accuracy only, 65% (13/20) reported process outcomes, 20% (4/20) documented clinical actions, and none measured patient outcomes. Methodological quality was concerning, as 70% (14/20) of studies were at high or very high risk of bias, with verification bias (14/20, 70%) and selection bias (10/20, 50%) being the most common. The overall certainty of evidence was very low-GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) ⊕◯◯◯, primarily due to risk of bias, heterogeneity, and imprecision.

[CONCLUSIONS] AI-assisted point-of-care imaging demonstrates promising diagnostic accuracy and enables meaningful task-shifting with minimal training requirements. However, critical evidence gaps remain, including absent patient outcome measurement, inadequate explainability evaluation, regulatory misalignment, and lack of cross-context validation despite claims of global applicability. Addressing these gaps requires implementation research with patient-outcome end points, rigorous XAI evaluation, and multicontext validation before widespread adoption. Limitations include restriction to English-language publications, gray literature exclusion, and heterogeneity precluding meta-analysis.