{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T01:44:07Z","timestamp":1786153447559,"version":"build-2736575974"},"reference-count":40,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Hum Factors"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>AI tools have the potential to enhance personalized clinical care, particularly in radiology. However, their integration into clinical workflows remains complex, especially in pediatric oncology, where early cancer detection is critical. Children with Li-Fraumeni syndrome (LFS), a rare cancer predisposition disorder, undergo regular surveillance whole-body magnetic resonance imaging (wbMRI), which presents an opportunity for AI-assisted tumor detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We evaluated the feasibility of an AI-assisted overlay for highlighting tumor-like regions in pediatric surveillance wbMRI and explored how access to the overlay influenced radiologist workflow, candidate-lesion marking behavior, follow-up recommendations, and perceived workload.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a patch-based AI segmentation model trained on augmented 2D slices from 675 surveillance wbMRI volumes of pediatric patients with LFS. The model was designed to highlight regions with high tumor probability. A reader study was conducted with 2 radiologists who independently reviewed wbMRI cases both with and without AI assistance. We measured evaluation time, number and location of reader-marked candidate lesions, type of follow-up recommendation, and subjective feedback using structured questionnaires.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AI assistance altered interpretation workflows for both radiologists, with mixed effects. On average, the time required to evaluate each case increased when using the AI tool for both radiologists. However, one radiologist had an increase in the number of candidate lesion locations selected with the tool, and one had a decrease in the number of candidate lesion locations selected with the tool. Subjective feedback indicated that one of the radiologists reported lower mental demand with the AI tool, while both radiologists reported lower stress with the AI tool. Interrater variability was evident, underscoring the need for personalized calibration of AI tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI-assisted wbMRI interpretation can improve tumor detection in pediatric cancer surveillance by reducing false negatives. However, its influence on workflow efficiency and interradiologist variability highlights the importance of careful implementation. Successful integration requires addressing challenges such as improving the predictive precision of AI models, offering intuitive end-user designs and instructions, and building trust in AI outputs. AI outputs can influence workflow and behavior in reader-specific ways. Clinical translation will require larger, randomized, multireader studies and model refinement to reduce false positives and quantify lesion-level reader performance. This can help ensure better patient outcomes in addition to reduced clinician burnout.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/81066","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T20:10:06Z","timestamp":1782418206000},"page":"e81066-e81066","source":"Crossref","is-referenced-by-count":0,"title":["Human-AI Interaction With AI-Assisted Tumor Overlays in Pediatric Whole-Body Magnetic Resonance Imaging: Exploratory Reader Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0332-6324","authenticated-orcid":false,"given":"Abhishek","family":"Moturu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-8152","authenticated-orcid":false,"given":"Olurotimi","family":"Komolafe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3199-168X","authenticated-orcid":false,"given":"Sayali","family":"Joshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-0447","authenticated-orcid":false,"given":"Andrea S","family":"Doria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2416-833X","authenticated-orcid":false,"given":"Anna","family":"Goldenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"issue":"1","key":"R1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-84698-5","article-title":"A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology","volume":"11","author":"Scheetz","journal-title":"Sci Rep"},{"issue":"8","key":"R2","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","journal-title":"Nat Rev Cancer"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4132\/jptm.2018.12.16","article-title":"Artificial intelligence in pathology","volume":"53","author":"Chang","journal-title":"J Pathol Transl Med"},{"issue":"3","key":"R4","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1111\/bjd.18880","article-title":"What is AI? 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