{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T00:36:03Z","timestamp":1793925363299,"version":"4.8.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Prim. Care Respir. Med."],"DOI":"10.1038\/s41533-026-00487-5","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T02:55:07Z","timestamp":1771901707000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The rise of artificial intelligence in respiratory primary care and pulmonology: a scoping review"],"prefix":"10.1038","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9740-2994","authenticated-orcid":false,"given":"Joan B.","family":"Soriano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-9027","authenticated-orcid":false,"given":"Sara","family":"Lumbreras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"487_CR1","first-page":"830","volume":"145","author":"EH Shortliffe","year":"1986","unstructured":"Shortliffe, E. H. Medical expert systems-knowledge tools for physicians. West. J. Med. 145, 830\u2013839 (1986).","journal-title":"West. J. Med."},{"key":"487_CR2","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/S0020-7373(78)80049-2","volume":"10","author":"W Van Melle","year":"1978","unstructured":"Van Melle, W. MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Intl J. Man-machine Stud. 10, 313\u2013322 (1978).","journal-title":"Intl J. Man-machine Stud."},{"key":"487_CR3","unstructured":"Rutledge G. W., Andersen S. K., Polaschek J. X., Fagan L. M. A belief network model for interpretation of ICU data. In Proceedings of the Annual Symposium on Computer Application in Medical Care 1990;(p. 785)."},{"key":"487_CR4","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1145\/219717.219768","volume":"38","author":"P Langley","year":"1995","unstructured":"Langley, P. & Simon, H. A. Applications of machine learning and rule induction. Commun. ACM 38, 54\u201364 (1995).","journal-title":"Commun. ACM"},{"key":"487_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.arbres.2022.07.007","volume":"59","author":"JL Izquierdo","year":"2023","unstructured":"Izquierdo, J. L., Oeste, C. L. & Hern\u00e1ndez Medrano, I. Artificial intelligence in pneumology: diagnostic and prognostic utilities. Arch. Bronconeumol. 59, 67\u201368, https:\/\/doi.org\/10.1016\/j.arbres.2022.07.007 (2023).","journal-title":"Arch. Bronconeumol."},{"key":"487_CR6","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.arbres.2023.03.017","volume":"59","author":"\u00c1 Fuentes-Mart\u00edn","year":"2023","unstructured":"Fuentes-Mart\u00edn, \u00c1, Cilleruelo-Ramos, \u00c1, Segura-M\u00e9ndez, B. & Mayol, J. Can an artificial intelligence model pass an examination for medical specialists?. Arch. Bronconeumol. 59, 534\u2013536, https:\/\/doi.org\/10.1016\/j.arbres.2023.03.017 (2023).","journal-title":"Arch. Bronconeumol."},{"key":"487_CR7","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, https:\/\/doi.org\/10.1136\/bmj.n71 (2021).","journal-title":"BMJ"},{"key":"487_CR8","unstructured":"Enhancing the QUAlity and Transparency Of health Research. https:\/\/www.equator-network.org\/reporting-guidelines\/prisma\/. Accessed on 9 December 2025"},{"key":"487_CR9","doi-asserted-by":"publisher","first-page":"e57271","DOI":"10.2196\/57271","volume":"13","author":"MM Alqahtani","year":"2024","unstructured":"Alqahtani, M. M. et al. Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review. Interact. J. Med. Res. 13, e57271, https:\/\/doi.org\/10.2196\/57271 (2024).","journal-title":"Interact. J. Med. Res."},{"key":"487_CR10","doi-asserted-by":"publisher","first-page":"3166","DOI":"10.1007\/s11606-020-06087-4","volume":"35","author":"F Toscano","year":"2020","unstructured":"Toscano, F. et al. How physicians spend their work time: an ecological momentary assessment. J. Gen. Intern. Med. 35, 3166\u20133172, https:\/\/doi.org\/10.1007\/s11606-020-06087-4 (2020).","journal-title":"J. Gen. Intern. Med."},{"key":"487_CR11","doi-asserted-by":"publisher","first-page":"e10010","DOI":"10.2196\/10010","volume":"7","author":"J Shen","year":"2019","unstructured":"Shen, J. et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med. Inf. 7, e10010, https:\/\/doi.org\/10.2196\/10010 (2019).","journal-title":"JMIR Med. Inf."},{"key":"487_CR12","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1148\/radiol.2018180921","volume":"291","author":"M Annarumma","year":"2019","unstructured":"Annarumma, M. et al. Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 291, 196\u2013202, https:\/\/doi.org\/10.1148\/radiol.2018180921 (2019).","journal-title":"Radiology"},{"key":"487_CR13","doi-asserted-by":"publisher","unstructured":"Wei C., et al Deep learning-based contour propagation in magnetic resonance imaging-guided radiotherapy of lung cancer patients. Phys Med Biol. 70. https:\/\/doi.org\/10.1088\/1361-6560\/ade8d0 (2025).","DOI":"10.1088\/1361-6560\/ade8d0"},{"key":"487_CR14","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s41747-020-00203-z","volume":"5","author":"I Castiglioni","year":"2021","unstructured":"Castiglioni, I. et al. Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. Eur. Radiol. Exp. 5, 7, https:\/\/doi.org\/10.1186\/s41747-020-00203-z (2021).","journal-title":"Eur. Radiol. Exp."},{"key":"487_CR15","doi-asserted-by":"publisher","first-page":"1060798","DOI":"10.3389\/fpubh.2022.1060798","volume":"10","author":"H Wang","year":"2022","unstructured":"Wang, H., Zhu, H. & Ding, L. Accurate classification of lung nodules on CT images using the TransUnet. Front. Public. Health 10, 1060798, https:\/\/doi.org\/10.3389\/fpubh.2022.1060798 (2022).","journal-title":"Front. Public. Health"},{"issue":"Suppl 2","key":"487_CR16","doi-asserted-by":"publisher","first-page":"S22","DOI":"10.1016\/j.arbres.2024.05.027","volume":"60","author":"S Baeza","year":"2024","unstructured":"Baeza, S. et al. Radiomics and clinical data for the diagnosis of incidental pulmonary nodules and lung cancer screening: radiolung integrative predictive model. Arch. Bronconeumol. 60(Suppl 2), S22\u2013S30, https:\/\/doi.org\/10.1016\/j.arbres.2024.05.027 (2024).","journal-title":"Arch. Bronconeumol."},{"key":"487_CR17","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1016\/j.jaip.2021.02.014","volume":"9","author":"A Kaplan","year":"2021","unstructured":"Kaplan, A. et al. Artificial intelligence\/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. J. Allergy Clin. Immunol. Pract. 9, 2255\u20132261, https:\/\/doi.org\/10.1016\/j.jaip.2021.02.014 (2021).","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"487_CR18","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1136\/bmj.39335.541782.AD","volume":"335","author":"E von Elm","year":"2007","unstructured":"von Elm, E. et al. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 335, 806\u2013808, https:\/\/doi.org\/10.1136\/bmj.39335.541782.AD (2007).","journal-title":"BMJ"},{"key":"487_CR19","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).","journal-title":"BMJ"},{"key":"487_CR20","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1513\/AnnalsATS.201605-418OC","volume":"13","author":"MJ Hegewald","year":"2016","unstructured":"Hegewald, M. J., Gallo, H. M. & Wilson, E. L. Accuracy and quality of spirometry in primary care offices. Ann. Am. Thorac. Soc. 13, 2119\u20132124, https:\/\/doi.org\/10.1513\/AnnalsATS.201605-418OC (2016).","journal-title":"Ann. Am. Thorac. Soc."},{"key":"487_CR21","doi-asserted-by":"publisher","first-page":"1801660","DOI":"10.1183\/13993003.01660-2018","volume":"53","author":"M Topalovic","year":"2019","unstructured":"Topalovic, M. et al. Pulmonary Function Study Investigators; Pulmonary Function Study Investigators. artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur. Respir. J. 53, 1801660, https:\/\/doi.org\/10.1183\/13993003.01660-2018 (2019).","journal-title":"Eur. Respir. J."},{"key":"487_CR22","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/s12931-022-02014-9","volume":"23","author":"Y Wang","year":"2022","unstructured":"Wang, Y. et al. Deep learning for spirometry quality assurance with spirometric indices and curves. Respir. Res. 23, 98, https:\/\/doi.org\/10.1186\/s12931-022-02014-9 (2022).","journal-title":"Respir. Res."},{"key":"487_CR23","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1136\/thorax-2024-221537","volume":"80","author":"D Gompelmann","year":"2025","unstructured":"Gompelmann, D. et al. AI-powered evaluation of lung function for diagnosis of interstitial lung disease. Thorax 80, 445\u2013450, https:\/\/doi.org\/10.1136\/thorax-2024-221537 (2025).","journal-title":"Thorax"},{"key":"487_CR24","unstructured":"Roundup: Pfizer finalises ResApp acquisition and more briefs. Available at: https:\/\/www.mobihealthnews.com\/tag\/resapp [Accessed on 22 August 2025]"},{"key":"487_CR25","doi-asserted-by":"publisher","first-page":"103304","DOI":"10.1016\/j.bspc.2021.103304","volume":"72","author":"M You","year":"2022","unstructured":"You, M. et al. Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression. Biomed. Signal. Process. Control. 72, 103304, https:\/\/doi.org\/10.1016\/j.bspc.2021.103304 (2022).","journal-title":"Biomed. Signal. Process. Control."},{"key":"487_CR26","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.3390\/s24041173","volume":"24","author":"P Kapetanidis","year":"2024","unstructured":"Kapetanidis, P. et al. Respiratory diseases diagnosis using audio analysis and artificial intelligence: a systematic review. Sensors 24, 1173, https:\/\/doi.org\/10.3390\/s24041173 (2024).","journal-title":"Sensors"},{"key":"487_CR27","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1038\/s41533-024-00392-9","volume":"34","author":"CH Huang","year":"2024","unstructured":"Huang, C. H. et al. The unreliability of crackles: insights from a breath sound study using physicians and artificial intelligence. NPJ Prim. Care Respir. Med. 34, 28, https:\/\/doi.org\/10.1038\/s41533-024-00392-9 (2024).","journal-title":"NPJ Prim. Care Respir. Med."},{"key":"487_CR28","doi-asserted-by":"publisher","first-page":"975","DOI":"10.3390\/diagnostics12040975","volume":"12","author":"KM Liao","year":"2022","unstructured":"Liao, K. M. et al. Development of an interactive AI system for the optimal timing prediction of successful weaning from mechanical ventilation for patients in respiratory care centers. Diagnostics 12, 975, https:\/\/doi.org\/10.3390\/diagnostics12040975 (2022).","journal-title":"Diagnostics"},{"key":"487_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s40635-023-00527-1","volume":"11","author":"LA Buiteman-Kruizinga","year":"2023","unstructured":"Buiteman-Kruizinga, L. A. et al. A closed-loop ventilation mode that targets the lowest work and force of breathing reduces the transpulmonary driving pressure in patients with moderate-to-severe ARDS. Intensive Care Med. Exp. 11, 42. https:\/\/doi.org\/10.1186\/s40635-023-00527-1 (2023).","journal-title":"Intensive Care Med. Exp."},{"key":"487_CR30","doi-asserted-by":"publisher","first-page":"e013935","DOI":"10.1136\/bmjopen-2016-013935","volume":"7","author":"PJ Honkoop","year":"2017","unstructured":"Honkoop, P. J. et al. MyAirCoach: the use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study. BMJ Open. 7, e013935, https:\/\/doi.org\/10.1136\/bmjopen-2016-013935 (2017).","journal-title":"BMJ Open."},{"key":"487_CR31","doi-asserted-by":"publisher","first-page":"1972","DOI":"10.1016\/j.jaip.2020.02.018","volume":"8","author":"RJ Khusial","year":"2020","unstructured":"Khusial, R. J. et al. Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma. J. Allergy Clin. Immunol. Pract. 8, 1972\u20131979.e8, https:\/\/doi.org\/10.1016\/j.jaip.2020.02.018 (2020).","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"487_CR32","doi-asserted-by":"publisher","first-page":"2562","DOI":"10.3390\/electronics11162562","volume":"11","author":"RT Bhowmik","year":"2022","unstructured":"Bhowmik, R. T. & Most, S. P. A personalized respiratory disease exacerbation prediction technique based on a novel spatio-temporal machine learning architecture and local environmental sensor networks. Electronics 11, 2562 (2022).","journal-title":"Electronics"},{"key":"487_CR33","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41533-025-00428-8","volume":"35","author":"H Tibble","year":"2025","unstructured":"Tibble, H., Sheikh, A. & Tsanas, A. Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care. NPJ Prim. Care Respir. Med. 35, 24, https:\/\/doi.org\/10.1038\/s41533-025-00428-8 (2025).","journal-title":"NPJ Prim. Care Respir. Med."},{"key":"487_CR34","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.rmed.2018.10.008","volume":"144","author":"JB Soriano","year":"2018","unstructured":"Soriano, J. B. et al. A multicentre, randomized controlled trial of telehealth for the management of COPD. Respir. Med. 144, 74\u201381, https:\/\/doi.org\/10.1016\/j.rmed.2018.10.008 (2018).","journal-title":"Respir. Med."},{"key":"487_CR35","doi-asserted-by":"publisher","first-page":"e9","DOI":"10.2196\/jmir.5729","volume":"19","author":"SS Tan","year":"2017","unstructured":"Tan, S. S. & Goonawardene, N. Internet health information seeking and the patient-physician relationship: a systematic review. J. Med. Internet Res. 19, e9 (2017).","journal-title":"J. Med. Internet Res."},{"key":"487_CR36","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1097\/MCP.0000000000000965","volume":"29","author":"H Pinnock","year":"2023","unstructured":"Pinnock, H., Hui, C. Y. & van Boven, J. F. Implementation of digital home monitoring and management of respiratory disease. Curr. Opin. Pulm. Med. 29, 302\u2013312 (2023).","journal-title":"Curr. Opin. Pulm. Med."},{"key":"487_CR37","doi-asserted-by":"publisher","first-page":"e21653","DOI":"10.2196\/21653","volume":"6","author":"JB Soriano","year":"2020","unstructured":"Soriano, J. B. et al. Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers. JMIR Public. Health Surveill. 6, e21653, https:\/\/doi.org\/10.2196\/21653 (2020).","journal-title":"JMIR Public. Health Surveill."},{"key":"487_CR38","doi-asserted-by":"publisher","first-page":"e12443","DOI":"10.2196\/12443","volume":"21","author":"KH Chu","year":"2019","unstructured":"Chu, K. H., Colditz, J., Malik, M., Yates, T. & Primack, B. Identifying key target audiences for public health campaigns: leveraging machine learning in the case of hookah tobacco smoking. J. Med. Internet Res. 21, e12443, https:\/\/doi.org\/10.2196\/12443 (2019).","journal-title":"J. Med. Internet Res."},{"key":"487_CR39","doi-asserted-by":"publisher","first-page":"2400919","DOI":"10.1183\/13993003.00919-2024","volume":"64","author":"JB Soriano","year":"2024","unstructured":"Soriano, J. B., Lumbreras, S., Celli, B. R. & Jenkins, C. R. I love you with all my lungs: a viewpoint on communicating effectively and positively about lung health. Eur. Respir. J. 64, 2400919, https:\/\/doi.org\/10.1183\/13993003.00919-2024 (2024).","journal-title":"Eur. Respir. J."},{"key":"487_CR40","doi-asserted-by":"publisher","first-page":"106418","DOI":"10.1016\/j.ypmed.2021.106418","volume":"145","author":"DH Han","year":"2021","unstructured":"Han, D. H., Lee, S. H., Lee, S. & Seo, D. C. Identifying emerging predictors for adolescent electronic nicotine delivery systems use: a machine learning analysis of the population assessment of tobacco and health study. Prev. Med. 145, 106418, https:\/\/doi.org\/10.1016\/j.ypmed.2021.106418 (2021).","journal-title":"Prev. Med."},{"key":"487_CR41","doi-asserted-by":"publisher","first-page":"738513","DOI":"10.3389\/fpubh.2021.738513","volume":"9","author":"R Benson","year":"2022","unstructured":"Benson, R., Hu, M., Chen, A. T., Zhu, S. H. & Conway, M. Examining cannabis, tobacco, and vaping discourse on reddit: an exploratory approach using natural language processing. Front. Public. Health 9, 738513, https:\/\/doi.org\/10.3389\/fpubh.2021.738513 (2022).","journal-title":"Front. Public. Health"},{"key":"487_CR42","doi-asserted-by":"publisher","first-page":"21","DOI":"10.24095\/hpcdp.42.1.04","volume":"42","author":"J Shi","year":"2022","unstructured":"Shi, J., Fu, R., Hamilton, H. & Chaiton, M. A machine learning approach to predict e-cigarette use and dependence among Ontario youth. Health Promot. Chronic Dis. Prev. Can. 42, 21\u201328, https:\/\/doi.org\/10.24095\/hpcdp.42.1.04 (2022).","journal-title":"Health Promot. Chronic Dis. Prev. Can."},{"key":"487_CR43","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1093\/ntr\/nty259","volume":"22","author":"LN Coughlin","year":"2020","unstructured":"Coughlin, L. N., Tegge, A. N., Sheffer, C. E. & Bickel, W. K. A machine-learning approach to predicting smoking cessation treatment outcomes. Nicotine Tob. Res. 22, 415\u2013422, https:\/\/doi.org\/10.1093\/ntr\/nty259 (2020).","journal-title":"Nicotine Tob. Res."},{"key":"487_CR44","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.5664\/jcsm.6186","volume":"12","author":"M Younes","year":"2016","unstructured":"Younes, M. & Hanly, P. J. Minimizing interrater variability in staging sleep by use of computer-derived features. J. Clin. Sleep. Med. 12, 1347\u20131356, https:\/\/doi.org\/10.5664\/jcsm.6186 (2016).","journal-title":"J. Clin. Sleep. Med."},{"key":"487_CR45","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1159\/000085205","volume":"51","author":"P Anderer","year":"2005","unstructured":"Anderer, P. et al. An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24\u2009\u00d7\u20097 utilizing the Siesta database. Neuropsychobiology. 51, 115\u2013133 (2005).","journal-title":"Neuropsychobiology."},{"key":"487_CR46","doi-asserted-by":"publisher","first-page":"7014","DOI":"10.3390\/s20247014","volume":"20","author":"A Manoni","year":"2020","unstructured":"Manoni, A. et al. A new wearable system for home sleep apnea testing, screening, and classification. Sensors 20, 7014, https:\/\/doi.org\/10.3390\/s20247014 (2020).","journal-title":"Sensors"},{"key":"487_CR47","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ejmp.2021.02.023","volume":"83","author":"CH Tsai","year":"2021","unstructured":"Tsai, C. H. et al. Automatic deep learning-based pleural effusion classification in lung ultrasound images for respiratory pathology diagnosis. Phys. Med. 83, 38\u201345, https:\/\/doi.org\/10.1016\/j.ejmp.2021.02.023 (2021).","journal-title":"Phys. Med."},{"key":"487_CR48","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.3390\/diagnostics11112049","volume":"11","author":"R Arntfield","year":"2021","unstructured":"Arntfield, R. et al. Automation of lung ultrasound interpretation via deep learning for the classification of normal versus abnormal lung parenchyma: a multicenter study. Diagnostics 11, 2049, https:\/\/doi.org\/10.3390\/diagnostics11112049 (2021).","journal-title":"Diagnostics"},{"key":"487_CR49","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1111\/resp.13842","volume":"25","author":"G Ferrante","year":"2020","unstructured":"Ferrante, G., Licari, A., Marseglia, G. L. & La Grutta, S. Artificial intelligence as an emerging diagnostic approach in paediatric pulmonology. Respirology 25, 1029\u20131030, https:\/\/doi.org\/10.1111\/resp.13842 (2020).","journal-title":"Respirology"},{"key":"487_CR50","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1007\/s00247-019-04593-0","volume":"50","author":"N Mahomed","year":"2020","unstructured":"Mahomed, N. et al. Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children. Pediatr. Radiol. 50, 482\u2013491, https:\/\/doi.org\/10.1007\/s00247-019-04593-0 (2020).","journal-title":"Pediatr. Radiol."},{"key":"487_CR51","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s12911-015-0208-9","volume":"15","author":"G Luo","year":"2015","unstructured":"Luo, G. et al. Predicting asthma control deterioration in children. BMC Med. Inf. Decis. Mak. 15, 84, https:\/\/doi.org\/10.1186\/s12911-015-0208-9 (2015).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"487_CR52","doi-asserted-by":"publisher","first-page":"e059929","DOI":"10.1136\/bmjopen-2021-059929","volume":"12","author":"BJH Dierick","year":"2022","unstructured":"Dierick, B. J. H. et al. Can electronic monitoring with a digital smart spacer support personalised medication adherence and inhaler technique education in patients with asthma?: Protocol of the randomised controlled OUTERSPACE trial. BMJ Open 12, e059929 (2022).","journal-title":"BMJ Open"},{"key":"487_CR53","doi-asserted-by":"publisher","first-page":"308","DOI":"10.2214\/AJR.18.20509","volume":"212","author":"S Aminololama-Shakeri","year":"2019","unstructured":"Aminololama-Shakeri, S. & L\u00f3pez, J. E. The doctor-patient relationship with artificial intelligence. AJR Am. J. Roentgenol. 212, 308\u2013310, https:\/\/doi.org\/10.2214\/AJR.18.20509 (2019).","journal-title":"AJR Am. J. Roentgenol."}],"container-title":["npj Primary Care Respiratory Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41533-026-00487-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41533-026-00487-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41533-026-00487-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:16:02Z","timestamp":1774984562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41533-026-00487-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["487"],"URL":"https:\/\/doi.org\/10.1038\/s41533-026-00487-5","relation":{},"ISSN":["2055-1010"],"issn-type":[{"value":"2055-1010","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]},"assertion":[{"value":"13 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}