{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T14:44:35Z","timestamp":1782917075530,"version":"3.54.5"},"reference-count":42,"publisher":"BMJ","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72404105"],"award-info":[{"award-number":["72404105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2024YFC231120"],"award-info":[{"award-number":["2024YFC231120"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2026,6,5]]},"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is increasingly used in tuberculosis (TB) diagnosis, but its clinical adoption depends not only on accuracy but also on physicians\u2019 trust and understanding of AI outputs. Explainable AI (XAI) has been proposed to address this challenge, but limited evidence exists on which explanation features are most valued by physicians in TB diagnostic settings. Discrete choice experiments (DCEs) offer a structured method to elicit physicians\u2019 preferences and quantify trade-offs among explainability attributes. This protocol describes a DCE designed to examine TB physicians\u2019 preferences for AI explainability to inform user-centred AI development and implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>Six attributes were identified through a comprehensive literature review, one-on-one semi-structured interviews with TB physicians and expert consultations. A D-efficient experimental design was used to construct choice sets. TB physicians in Hubei Province, China, will be recruited using a stratified random sampling approach. Preference data will be analysed using multinomial logit and mixed logit models to estimate the relative importance of attributes and explore preference heterogeneity across physician subgroups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>Ethical approval has been obtained from the Ethics Committee of Wuhan Pulmonary Hospital. All participants will provide written informed consent prior to participation. Study findings will be disseminated through peer-reviewed journal publications, conference presentations and academic forums, with the aim of informing the design and implementation of XAI systems for TB diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2026-116656","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T13:51:58Z","timestamp":1782913918000},"page":"e116656","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring tuberculosis physicians\u2019 preferences for AI explainability in China: a protocol for a discrete choice experiment"],"prefix":"10.1136","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1902-0474","authenticated-orcid":false,"given":"Jiale","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6385-0502","authenticated-orcid":false,"given":"Qian","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan Pulmonary Hospital, Medical Department, Jianghan University, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"2026070106251627000_16.7.e116656.1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/S2213-2600(24)00028-6","article-title":"Classification of early tuberculosis states to guide research for improved care and prevention: an international Delphi consensus exercise","volume":"12","author":"Coussens","year":"2024","journal-title":"Lancet Respir Med"},{"key":"2026070106251627000_16.7.e116656.2","unstructured":"World Health Organization . Global tuberculosis report 2024. 2024. Available: https:\/\/www.who.int\/teams\/global-programme-on-tuberculosis-and-lung-health\/tb-reports\/global-tuberculosis-report-2024 [accessed 30 Nov 2025]"},{"key":"2026070106251627000_16.7.e116656.3","first-page":"92","article-title":"WHO Tuberculosis Report: key data analysis for China and the global world","volume":"9","author":"Yuanyuan","year":"2024","journal-title":"Electronic Journal of Emerging Infectious Diseases"},{"key":"2026070106251627000_16.7.e116656.4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2012.01773.x"},{"key":"2026070106251627000_16.7.e116656.5","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.4103\/0366-6999.182840","article-title":"Clinical and Laboratory Diagnosis of Intestinal Tuberculosis","volume":"129","author":"Shi","year":"2016","journal-title":"Chin Med J"},{"key":"2026070106251627000_16.7.e116656.6","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/S1473-3099(20)30400-X","article-title":"The uncertainty of tuberculosis diagnosis","volume":"20","author":"Datta","year":"2020","journal-title":"Lancet Infect Dis"},{"key":"2026070106251627000_16.7.e116656.7","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(18)30111-7"},{"key":"2026070106251627000_16.7.e116656.8","doi-asserted-by":"crossref","first-page":"S21","DOI":"10.1093\/infdis\/jiu803","article-title":"Tuberculosis diagnostics in 2015: landscape, priorities, needs, and prospects","volume":"211 Suppl 2","author":"Pai","year":"2015","journal-title":"J Infect Dis"},{"key":"2026070106251627000_16.7.e116656.9","unstructured":"World Health Organization . WHO consolidated guidelines on tuberculosis: Module 2: screening \u2013 systematic screening for tuberculosis disease; Geneva World Health Organization; 2021."},{"key":"2026070106251627000_16.7.e116656.10","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s11831-023-09987-w","article-title":"A Comprehensive Review on Advancement in Deep Learning Techniques for Automatic Detection of Tuberculosis from Chest X-ray Images","volume":"31","author":"Kotei","year":"2024","journal-title":"Arch Computat Methods Eng"},{"key":"2026070106251627000_16.7.e116656.11","article-title":"From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning","volume":"10","author":"Li","year":"2023","journal-title":"Mil Med Res"},{"key":"2026070106251627000_16.7.e116656.12","doi-asserted-by":"crossref","first-page":"105941","DOI":"10.1016\/j.bspc.2023.105941","article-title":"Lung disease classification using chest X ray image: An optimal ensemble of classification with hybrid training","volume":"91","author":"Dharmesh Ishwerlal","year":"2024","journal-title":"Biomed Signal Process Control"},{"key":"2026070106251627000_16.7.e116656.13","doi-asserted-by":"publisher","DOI":"10.21037\/jtd-2025-604"},{"key":"2026070106251627000_16.7.e116656.14","doi-asserted-by":"crossref","first-page":"104667","DOI":"10.1016\/j.bspc.2023.104667","article-title":"Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach","volume":"84","author":"Iqbal","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"2026070106251627000_16.7.e116656.15","doi-asserted-by":"crossref","first-page":"e214","DOI":"10.1016\/S2589-7500(22)00029-2","article-title":"Explainability and artificial intelligence in medicine","volume":"4","author":"Reddy","year":"2022","journal-title":"Lancet Digit Health"},{"key":"2026070106251627000_16.7.e116656.16","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ins.2022.10.013","article-title":"Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey","volume":"615","author":"Ding","year":"2022","journal-title":"Inf Sci (Ny)"},{"key":"2026070106251627000_16.7.e116656.17","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"2026070106251627000_16.7.e116656.18","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"2026070106251627000_16.7.e116656.19","doi-asserted-by":"crossref","first-page":"103428","DOI":"10.1016\/j.artint.2020.103428","article-title":"Evaluating local explanation methods on ground truth","volume":"291","author":"Guidotti","year":"2021","journal-title":"Artif Intell"},{"key":"2026070106251627000_16.7.e116656.20","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence","volume":"16","author":"Hassija","year":"2024","journal-title":"Cogn Comput"},{"key":"2026070106251627000_16.7.e116656.21","doi-asserted-by":"crossref","DOI":"10.2196\/62768","article-title":"The Willingness of Doctors to Adopt Artificial Intelligence-Driven Clinical Decision Support Systems at Different Hospitals in China: Fuzzy Set Qualitative Comparative Analysis of Survey Data","volume":"27","author":"Yu","year":"2025","journal-title":"J Med Internet Res"},{"key":"2026070106251627000_16.7.e116656.22","doi-asserted-by":"crossref","DOI":"10.1186\/s12913-022-07769-x","article-title":"Physicians\u2019 preferences and willingness to pay for artificial intelligence-based assistance tools: a discrete choice experiment among german radiologists","volume":"22","author":"von Wedel","year":"2022","journal-title":"BMC Health Serv Res"},{"key":"2026070106251627000_16.7.e116656.23","doi-asserted-by":"crossref","DOI":"10.2196\/32939","article-title":"Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review","volume":"24","author":"Chew","year":"2022","journal-title":"J Med Internet Res"},{"key":"2026070106251627000_16.7.e116656.24","doi-asserted-by":"crossref","DOI":"10.2196\/69068","article-title":"Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review","volume":"27","author":"Hansun","year":"2025","journal-title":"J Med Internet Res"},{"key":"2026070106251627000_16.7.e116656.25","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000067","article-title":"Comparing different versions of computer-aided detection products when reading chest X-rays for tuberculosis","volume":"1","author":"Qin","year":"2022","journal-title":"PLOS Digit Health"},{"key":"2026070106251627000_16.7.e116656.26","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-025-01958-8","article-title":"Comparison of SHAP and clinician friendly explanations reveals effects on clinical decision behaviour","volume":"8","author":"Hur","year":"2025","journal-title":"NPJ Digit Med"},{"key":"2026070106251627000_16.7.e116656.27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3631614","article-title":"Meaningful Explanation Effect on User\u2019s Trust in an AI Medical System: Designing Explanations for Non-Expert Users","volume":"13","author":"Larasati","year":"2023","journal-title":"ACM Trans Interact Intell Syst"},{"key":"2026070106251627000_16.7.e116656.28","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.233261","article-title":"Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AI","volume":"313","author":"Prinster","year":"2024","journal-title":"Radiology"},{"key":"2026070106251627000_16.7.e116656.29","doi-asserted-by":"crossref","DOI":"10.2196\/77491","article-title":"Preferences of Patients With Tuberculosis for AI-Assisted Remote Health Management: Discrete Choice Experiment","volume":"27","author":"Xu","year":"2025","journal-title":"J Med Internet Res"},{"key":"2026070106251627000_16.7.e116656.30","doi-asserted-by":"crossref","DOI":"10.1186\/1472-6963-14-367","article-title":"The use of discrete choice experiments to inform health workforce policy: a systematic review","volume":"14","author":"Mandeville","year":"2014","journal-title":"BMC Health Serv Res"},{"key":"2026070106251627000_16.7.e116656.31","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s40120-023-00453-0","article-title":"Discrete Choice Experiment to Understand Japanese Patients\u2019 and Physicians\u2019 Preferences for Preventive Treatments for Migraine","volume":"12","author":"Seo","year":"2023","journal-title":"Neurol Ther"},{"key":"2026070106251627000_16.7.e116656.32","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1159\/000546693","article-title":"Patient and Physician Preferences for Add-On Systemic Therapy to Transarterial Chemoembolization for Hepatocellular Carcinoma in Japan: A Discrete Choice Experiment","volume":"15","author":"Furuse","year":"2026","journal-title":"Liver Cancer"},{"key":"2026070106251627000_16.7.e116656.33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2010.11.013"},{"key":"2026070106251627000_16.7.e116656.34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2012.08.2223"},{"key":"2026070106251627000_16.7.e116656.35","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2025-101918","article-title":"Exploring tuberculosis patients\u2019 preferences for AI-assisted remote health management services in China: a protocol for a discrete choice experiment","volume":"15","author":"Wang","year":"2025","journal-title":"BMJ Open"},{"key":"2026070106251627000_16.7.e116656.36","doi-asserted-by":"crossref","DOI":"10.1177\/002224379603300305","article-title":"The Importance of Utility Balance in Efficient Choice Designs","volume":"33","author":"Huber","year":"1996","journal-title":"Journal of Marketing Research"},{"key":"2026070106251627000_16.7.e116656.37","doi-asserted-by":"publisher","DOI":"10.1007\/s40271-015-0118-z"},{"key":"2026070106251627000_16.7.e116656.38","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2022-066783","article-title":"Preferences for accessing sexual health services among middle-aged and older adults in the UK: a study protocol for a discrete choice experiment using mixed methods","volume":"13","author":"Kpokiri","year":"2023","journal-title":"BMJ Open"},{"key":"2026070106251627000_16.7.e116656.39","doi-asserted-by":"publisher","DOI":"10.1007\/s40273-018-0734-2"},{"key":"2026070106251627000_16.7.e116656.40","doi-asserted-by":"crossref","first-page":"14000","DOI":"10.1080\/10447318.2025.2480845","article-title":"Bridging the Interpretability Gap: Public Preferences for Explainable Artificial Intelligence in Public Service Decision-Making","volume":"41","author":"Ma","year":"2025","journal-title":"Int J Hum-Comput Interact"},{"key":"2026070106251627000_16.7.e116656.41","doi-asserted-by":"crossref","first-page":"122343","DOI":"10.1016\/j.techfore.2023.122343","article-title":"How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence","volume":"188","author":"Kim","year":"2023","journal-title":"Technol Forecast Soc Change"},{"key":"2026070106251627000_16.7.e116656.42","doi-asserted-by":"crossref","unstructured":"Naiseh M , Al-Thani D , Jiang N , et al . How the different explanation classes impact trust calibration: The case of clinical decision support systems. Int J Hum Comput Stud 2023;169. doi:10.1016\/j.ijhcs.2022.102941","DOI":"10.1016\/j.ijhcs.2022.102941"}],"container-title":["BMJ Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/bmjopen-2026-116656","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T13:52:14Z","timestamp":1782913934000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmjopen.bmj.com\/lookup\/doi\/10.1136\/bmjopen-2026-116656"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":42,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,1]]},"published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1136\/bmjopen-2026-116656"],"URL":"https:\/\/doi.org\/10.1136\/bmjopen-2026-116656","relation":{},"ISSN":["2044-6055","2044-6055"],"issn-type":[{"value":"2044-6055","type":"print"},{"value":"2044-6055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]}}}