{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:08:11Z","timestamp":1780430891807,"version":"3.54.1"},"reference-count":11,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Hum Factors"],"abstract":"\n Background<\/jats:title>\n Artificial intelligence (AI) detection tools for intracranial hemorrhage (ICH) are increasingly integrated into radiology workflows. In real-world practice, perceived utility depends not only on diagnostic performance but also on workflow fit, false positive burden, and how clinicians interpret and act on AI outputs.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to characterize radiologists\u2019 perceptions of a Food and Drug Administration (FDA)\u2013cleared ICH AI detection tool in a national teleradiology network, including perceived reliability, false positive burden, workflow impact, medicolegal concerns, and self-reported behaviors during routine use.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We conducted an anonymous cross-sectional survey of radiologists in a national teleradiology practice who had access to an FDA-cleared ICH AI overlay during noncontrast head computed tomography interpretation. Survey items used a 5-point Likert scale. Results are summarized as agreement proportions (\u201cagree\u201d or \u201cstrongly agree\u201d) with 95% CIs. We compared neuroradiologists with non-neuroradiologists using Fisher exact tests. One primary end point was prespecified: agreement that time spent reviewing examinations with false positive AI alerts outweighed the benefits. Remaining subgroup comparisons were treated as exploratory, with false discovery rate control using the Benjamini-Hochberg procedure.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 65 radiologists responded, including 23 (35.4%) neuroradiologists and 42 (64.6%) non-neuroradiologists. Only 18.5% (12\/65; 95% CI 10.9%-29.6%) agreed that false-positive alerts were infrequent enough to be acceptable. Agreement that the AI correctly identified most ICH cases was 32.3% (21\/65; 95% CI 22.2%-44.4%), and agreement that the AI rarely missed clinically important hemorrhages was 43.1% (28\/65; 95% CI 31.8%-55.2%). Trust in AI output was conditional: 50.8% (33\/65; 95% CI 38.9%-62.5%) reported trusting the AI when it agreed with their interpretation, whereas 3.1% (2\/65; 95% CI 0.8%-10.5%) reported trusting it when it conflicted with their interpretation. Only 10.8% (7\/65; 95% CI 5.3%-20.6%) reported reduced overall interpretation time, whereas 33.8% (22\/65; 95% CI 23.5%-46.0%) agreed that time spent reviewing false-positive alerts outweighed the benefits. Self-reported reduced scrutiny after an AI-negative result was uncommon (4\/65, 6.2%; 95% CI 2.4%-14.8%). In subgroup analysis, neuroradiologists more often endorsed the primary end point than non-neuroradiologists (12\/23, 52.2% vs 10\/42, 23.8%; unadjusted P=.03), but no exploratory subgroup differences remained statistically significant after false discovery rate correction. Free-text responses emphasized artifact- and calcification-driven false positives, delayed or inconsistent AI availability, consultation burden, and medicolegal concerns.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n In this national teleradiology setting, radiologists reported substantial false positive burden, limited perceived time savings, and strongly conditional trust in an FDA-cleared ICH AI detection tool. Self-reported reduced scrutiny after negative AI outputs was uncommon but present in a minority of cases. These findings support the importance of specificity, interpretability, latency, and workflow-aware implementation when deploying radiology AI tools in practice.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/92145","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T20:30:05Z","timestamp":1777926605000},"page":"e92145","source":"Crossref","is-referenced-by-count":0,"title":["Radiologist Perceptions of an AI Tool for Intracranial Hemorrhage Detection in Teleradiology: Cross-Sectional Survey Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4438-2082","authenticated-orcid":false,"given":"Andrew J","family":"Del Gaizo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2235-3907","authenticated-orcid":false,"given":"Jackson K","family":"Del Gaizo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9837-3409","authenticated-orcid":false,"given":"Troy A","family":"Shahoumian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.240067"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/bjro\/tzae033"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2025.07.055"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A8986"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2022-070904"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-11332-z"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1097\/nci.0b013e3182a903f9"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-017-0430-8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09747-1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1518\/001872097778543886"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/S1071-5819(03)00038-7"}],"container-title":["JMIR Human Factors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/humanfactors.jmir.org\/2026\/1\/e92145","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T19:35:07Z","timestamp":1780428907000},"score":1,"resource":{"primary":{"URL":"https:\/\/humanfactors.jmir.org\/2026\/1\/e92145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,2]]},"references-count":11,"URL":"https:\/\/doi.org\/10.2196\/92145","relation":{"has-preprint":[{"id-type":"doi","id":"10.2196\/preprints.92145","asserted-by":"object"}]},"ISSN":["2292-9495"],"issn-type":[{"value":"2292-9495","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,2]]}}}