{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T11:32:00Z","timestamp":1767007920127,"version":"3.48.0"},"reference-count":11,"publisher":"BMJ","issue":"12","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"unspecified","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019703","name":"Lunit","doi-asserted-by":"publisher","award":["Commercial research grant amounting to \u00a35,500.00"],"award-info":[{"award-number":["Commercial research grant amounting to \u00a35,500.00"]}],"id":[{"id":"10.13039\/100019703","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2024,10,25]]},"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. There is a paucity of evidence on the impact of AI algorithms in assisting healthcare professionals (other than radiologists) who regularly review CXR images in their daily practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To assess the utility of an AI-based CXR interpretation tool in assisting the diagnostic accuracy, speed and confidence of a varied group of healthcare professionals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>The study will be conducted using 500 retrospectively collected inpatient and emergency department CXRs from two UK hospital trusts. Two fellowship-trained thoracic radiologists with at least 5 years of experience will independently review all studies to establish the ground truth reference standard with arbitration from a third senior radiologist in case of disagreement. The Lunit INSIGHT CXR tool (Seoul, Republic of Korea) will be applied and compared against the reference standard. Area under the receiver operating characteristic curve (AUROC) will be calculated for 10 abnormal findings: pulmonary nodules\/mass, consolidation, pneumothorax, atelectasis, calcification, cardiomegaly, fibrosis, mediastinal widening, pleural effusion and pneumoperitoneum. Performance testing will be carried out with readers from various clinical professional groups with and without the assistance of Lunit INSIGHT CXR to evaluate the utility of the algorithm in improving reader accuracy (sensitivity, specificity, AUROC), confidence and speed (paired sample t-test). The study is currently ongoing with a planned end date of 31 December 2024.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>The study has been approved by the UK Healthcare Research Authority. The use of anonymised retrospective CXRs has been authorised by Oxford University Hospital\u2019s information governance teams. The results will be presented at relevant conferences and published in a peer-reviewed journal.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial registration number<\/jats:title>\n                    <jats:p>Protocol ID 310995-B (awaiting approval), ClinicalTrials.gov<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2023-080554","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T02:58:50Z","timestamp":1735009130000},"page":"e080554","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":6,"title":["AI-assisted detection for chest X-rays (AID-CXR): a multi-reader multi-case study protocol"],"prefix":"10.1136","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2308-414X","authenticated-orcid":false,"given":"Farhaan","family":"Khan","sequence":"first","affiliation":[{"name":"Oxford University Hospitals NHS Foundation Trust, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indrajeet","family":"Das","sequence":"additional","affiliation":[{"name":"University Hospitals of Leicester NHS Trust, Leicester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marusa","family":"Kotnik","sequence":"additional","affiliation":[{"name":"Addenbrooke's Hospital, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louise","family":"Wing","sequence":"additional","affiliation":[{"name":"Oxford University Hospitals NHS Foundation Trust, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edwin","family":"Van Beek","sequence":"additional","affiliation":[{"name":"Edinburgh Imaging, Queen\u2019s Medical Research Institute, University of Edinburgh, Edinburgh, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Murchison","sequence":"additional","affiliation":[{"name":"Royal Infirmary of Edinburgh, Edinburgh, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong Seok","family":"Ahn","sequence":"additional","affiliation":[{"name":"Lunit Inc, Gangnam-gu, Seoul, Korea (the Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang Hyup","family":"Lee","sequence":"additional","affiliation":[{"name":"Lunit Inc, Gangnam-gu, Seoul, Korea (the Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ambika","family":"Seth","sequence":"additional","affiliation":[{"name":"Lunit Inc, Gangnam-gu, Seoul, Korea (the Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0967-3554","authenticated-orcid":false,"given":"Abdala Trinidad","family":"Espinosa Morgado","sequence":"additional","affiliation":[{"name":"Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0582-9518","authenticated-orcid":false,"given":"Howell","family":"Fu","sequence":"additional","affiliation":[{"name":"Oxford University Hospitals NHS Foundation Trust, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5880-8235","authenticated-orcid":false,"given":"Alex","family":"Novak","sequence":"additional","affiliation":[{"name":"Emergency Medicine Research Oxford, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabeeha","family":"Salik","sequence":"additional","affiliation":[{"name":"RAIQC Ltd, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Campbell","sequence":"additional","affiliation":[{"name":"Radiology, University College London Hospitals NHS Foundation Trust, London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruchir","family":"Shah","sequence":"additional","affiliation":[{"name":"Oxford University Hospitals NHS Foundation Trust, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fergus","family":"Gleeson","sequence":"additional","affiliation":[{"name":"Churchill Hospital, Oxford, Oxfordshire, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-5033","authenticated-orcid":false,"given":"Sarim","family":"Ather","sequence":"additional","affiliation":[{"name":"Oxford University Hospitals NHS Foundation Trust, Oxford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"2025121410514062000_14.12.e080554.1","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1111\/1754-9485.13274","article-title":"Chest radiographs and machine learning - Past, present and future","volume":"65","author":"Jones","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"key":"2025121410514062000_14.12.e080554.2","doi-asserted-by":"crossref","first-page":"743","DOI":"10.3390\/diagnostics13040743","article-title":"Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review","volume":"13","author":"Ahmad","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"2025121410514062000_14.12.e080554.3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.crad.2022.08.129","article-title":"Validation study of machine-learning chest radiograph software in primary and emergency medicine","volume":"78","author":"van Beek","year":"2023","journal-title":"Clin Radiol"},{"key":"2025121410514062000_14.12.e080554.4","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0256630","article-title":"Pneumonia detection in chest X-ray images using an ensemble of deep learning models","volume":"16","author":"Kundu","year":"2021","journal-title":"PLoS One"},{"key":"2025121410514062000_14.12.e080554.5","doi-asserted-by":"publisher","DOI":"10.1536\/ihj.19-714"},{"key":"2025121410514062000_14.12.e080554.6","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2022.47172","article-title":"Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs","volume":"5","author":"Hillis","year":"2022","journal-title":"JAMA Netw Open"},{"key":"2025121410514062000_14.12.e080554.7","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.41096","article-title":"An Artificial Intelligence-Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study","volume":"4","author":"Homayounieh","year":"2021","journal-title":"JAMA Netw Open"},{"key":"2025121410514062000_14.12.e080554.8","unstructured":"Artificial intelligence for analysing chest x-ray images. NICE; 2022. Available: https:\/\/www.nice.org.uk\/advice\/mib292"},{"key":"2025121410514062000_14.12.e080554.9","first-page":"49","article-title":"X-ray misinterpretation in urgent care: where does it occur, why does it occur, and does it matter?","volume":"135","author":"Wilson","year":"2022","journal-title":"N Z Med J"},{"key":"2025121410514062000_14.12.e080554.10","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.22779","article-title":"Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents","volume":"3","author":"Wu","year":"2020","journal-title":"JAMA Netw Open"},{"key":"2025121410514062000_14.12.e080554.11","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study","volume":"15","author":"Zech","year":"2018","journal-title":"PLoS Med"}],"container-title":["BMJ Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjopen-2023-080554","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T18:51:47Z","timestamp":1765738307000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopen.bmj.com\/lookup\/doi\/10.1136\/bmjopen-2023-080554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":11,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12,20]]},"published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10.1136\/bmjopen-2023-080554"],"URL":"https:\/\/doi.org\/10.1136\/bmjopen-2023-080554","relation":{},"ISSN":["2044-6055","2044-6055"],"issn-type":[{"type":"print","value":"2044-6055"},{"type":"electronic","value":"2044-6055"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}