{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:06:02Z","timestamp":1782846362132,"version":"3.54.5"},"reference-count":30,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We aim to systematically evaluate and synthesize evidence on AI-based clinical decision support systems using point-of-care imaging.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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\u2013capable 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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\u2014GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) \u2295\u25ef\u25ef\u25ef, primarily due to risk of bias, heterogeneity, and imprecision.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/80928","type":"journal-article","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T07:55:05Z","timestamp":1770450905000},"page":"e80928-e80928","source":"Crossref","is-referenced-by-count":0,"title":["AI in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability"],"prefix":"10.2196","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3963-4209","authenticated-orcid":false,"given":"Peter","family":"Wadie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5746-0196","authenticated-orcid":false,"given":"Bishoy","family":"Zakher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5892-632X","authenticated-orcid":false,"given":"Khalid","family":"Elgazzar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8048-5635","authenticated-orcid":false,"given":"Abdulhamid","family":"Alsbakhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2185-6831","authenticated-orcid":false,"given":"Abdul-Mohsen G","family":"Alhejaily","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"issue":"11","key":"R1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.72896","article-title":"Automated analysis of ultrasound for the diagnosis of pneumothorax: a systematic review","volume":"16","author":"Kossoff","journal-title":"Cureus"},{"issue":"176","key":"R2","doi-asserted-by":"publisher","DOI":"10.1183\/16000617.0274-2024","article-title":"Artificial intelligence in bronchoscopy: a systematic review","volume":"34","author":"Cold","journal-title":"Eur Respir Rev"},{"issue":"9","key":"R3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000783","article-title":"Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: a systematic review","volume":"4","author":"Rambabu","journal-title":"PLOS Digit Health"},{"issue":"9","key":"R4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0291972","article-title":"CD44-SNA1 integrated cytopathology for delineation of high grade dysplastic and neoplastic oral lesions","volume":"18","author":"Sunny","journal-title":"PLOS ONE"},{"key":"R5","unstructured":"Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 june 2024. 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