{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T11:43:16Z","timestamp":1792669396639,"version":"4.5.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eat Weight Disord"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) applications in eating disorder research have expanded rapidly, yet the structure, scope, and translational readiness of this literature have not been systematically mapped. This study aimed to characterise publication trends, AI methodologies, clinical application domains, data sources, and implementation readiness across the empirical literature on AI in eating disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A scoping review with bibliometric analysis was conducted across three databases (PubMed [MEDLINE], the Web of Science Core Collection, and Scopus) on 20 November 2025. Studies were included if they reported original empirical research employing AI or machine learning as the primary analytical method in an eating disorder population. A total of 66 articles published between 2016 and 2025 were included. Each study was classified according to predefined taxonomies covering AI methodology, clinical application domain, data source type, implementation readiness, eating disorder type, and target population. Bibliometric indicators, including total citations, citations per year, and normalised citation counts, were retrieved from the Web of Science Core Collection. Co-authorship, keyword co-occurrence, bibliographic coupling, and co-citation networks were constructed using Python (version 3.11) with the NetworkX and matplotlib libraries, and reporting quality was appraised using the TRIPOD+AI and CONSORT-AI statements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Publication output grew from one article in 2016 to 16 in 2025. Machine learning was the dominant AI methodology (78.8%), followed by natural language processing (19.7%) and a single conversational AI application (1.5%). The most common clinical applications were risk prediction (33.3%), diagnosis (25.8%), and treatment outcome prediction (21.2%). Clinical data (42.4%) and neuroimaging (18.2%) were the most frequently used data sources. The majority of studies remained at the laboratory stage (77.3%), with only two describing clinically deployed systems. The United States contributed 33.3% of articles; no contributions were identified from Africa, South America, or Southeast Asia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI research in eating disorders is growing rapidly but remains geographically concentrated, methodologically dominated by traditional machine learning, and largely confined to laboratory settings. Adolescent populations (despite representing the peak age of eating disorder onset), bulimia nervosa, and low- and middle-income country contexts are systematically underrepresented. Closing the gap between research output and clinical implementation will require independent model validation, prospective evaluation, and sustained multidisciplinary collaboration.<\/jats:p>\n                    <jats:p>\n                      <jats:italic>Level of Evidence<\/jats:italic>\n                      Not applicable. This is a scoping review with bibliometric analysis that maps the scope and structure of a research field rather than evaluating clinical effectiveness; it therefore does not correspond to a traditional level-of-evidence rating.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s40519-026-01905-w","type":"journal-article","created":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T05:54:50Z","timestamp":1789883690000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in eating disorder research: a scoping review with bibliometric analysis of trends, methodologies, and translational readiness (2016\u20132025)"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6550-1813","authenticated-orcid":false,"given":"Yasin","family":"Caliskan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9433-1892","authenticated-orcid":false,"given":"Mesut","family":"Sari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0162-4574","authenticated-orcid":false,"given":"H\u00fclya","family":"Binokay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"1905_CR1","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1001\/archgenpsychiatry.2011.74","volume":"68","author":"J Arcelus","year":"2011","unstructured":"Arcelus J, Mitchell AJ, Wales J, Nielsen S (2011) Mortality rates in patients with anorexia nervosa and other eating disorders. A meta-analysis of 36 studies. Arch Gen Psychiatry 68:724\u2013731. https:\/\/doi.org\/10.1001\/archgenpsychiatry.2011.74","journal-title":"Arch Gen Psychiatry"},{"key":"1905_CR2","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1016\/S0140-6736(20)30059-3","volume":"395","author":"J Treasure","year":"2020","unstructured":"Treasure J, Duarte TA, Schmidt U (2020) Eating disorders. Lancet 395:899\u2013911. https:\/\/doi.org\/10.1016\/S0140-6736(20)30059-3","journal-title":"Lancet"},{"key":"1905_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpr.2025.102547","volume":"116","author":"I Krug","year":"2025","unstructured":"Krug I, Liu S, Portingale J, Croce S, Dar B, Obleada K et al (2025) A meta-analysis of mortality rates in eating disorders: an update of the literature from 2010 to 2024. Clin Psychol Rev 116:102547. https:\/\/doi.org\/10.1016\/j.cpr.2025.102547","journal-title":"Clin Psychol Rev"},{"key":"1905_CR4","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s40519-021-01162-z","volume":"27","author":"J Qian","year":"2022","unstructured":"Qian J, Wu Y, Liu F, Zhu Y, Jin H, Zhang H et al (2022) An update on the prevalence of eating disorders in the general population: a systematic review and meta-analysis. Eat Weight Disord 27:415\u2013428. https:\/\/doi.org\/10.1007\/s40519-021-01162-z","journal-title":"Eat Weight Disord"},{"key":"1905_CR5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1002\/wps.21182","volume":"23","author":"M Solmi","year":"2024","unstructured":"Solmi M, Monaco F, H\u00f8jlund M, Monteleone AM, Trott M, Firth J et al (2024) Outcomes in people with eating disorders: a transdiagnostic and disorder-specific systematic review, meta-analysis and multivariable meta-regression analysis. World Psychiatry 23:124\u2013138. https:\/\/doi.org\/10.1002\/wps.21182","journal-title":"World Psychiatry"},{"key":"1905_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/s40337-022-00654-2","volume":"10","author":"A Hambleton","year":"2022","unstructured":"Hambleton A, Pepin G, Le A, Maloney D, National Eating Disorder Research Consortium, Aouad P et al (2022) Psychiatric and medical comorbidities of eating disorders: findings from a rapid review of the literature. J Eat Disord 10:132. https:\/\/doi.org\/10.1186\/s40337-022-00654-2","journal-title":"J Eat Disord"},{"key":"1905_CR7","doi-asserted-by":"publisher","unstructured":"Bulik CM, Blake L, Austin J (2019) Genetics of eating disorders: what the clinician needs to know. Psychiatr Clin North Am 42:59\u201373. https:\/\/doi.org\/10.1016\/j.psc.2018.10.007","DOI":"10.1016\/j.psc.2018.10.007"},{"key":"1905_CR8","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/s40337-022-00581-2","volume":"10","author":"J Fardouly","year":"2022","unstructured":"Fardouly J, Crosby RD, Sukunesan S (2022) Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions. J Eat Disord 10:66. https:\/\/doi.org\/10.1186\/s40337-022-00581-2","journal-title":"J Eat Disord"},{"key":"1905_CR9","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1002\/eat.24385","volume":"58","author":"J Linardon","year":"2025","unstructured":"Linardon J, Liu C, Messer M, McClure Z, Anderson C, Jarman HK (2025) Current practices and perspectives of artificial intelligence in the clinical management of eating disorders: insights from clinicians and community participants. Int J Eat Disord 58:724\u2013734. https:\/\/doi.org\/10.1002\/eat.24385","journal-title":"Int J Eat Disord"},{"key":"1905_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s11920-019-1094-0","volume":"21","author":"S Graham","year":"2019","unstructured":"Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC et al (2019) Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep 21:116. https:\/\/doi.org\/10.1007\/s11920-019-1094-0","journal-title":"Curr Psychiatry Rep"},{"key":"1905_CR11","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1002\/wps.20883","volume":"20","author":"J Torous","year":"2021","unstructured":"Torous J, Bucci S, Bell IH, Kessing LV, Faurholt\u2010Jepsen M, Whelan P et al (2021) The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 20:318\u2013335. https:\/\/doi.org\/10.1002\/wps.20883","journal-title":"World Psychiatry"},{"key":"1905_CR12","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1002\/eat.22828","volume":"51","author":"L Lavagnino","year":"2018","unstructured":"Lavagnino L, Mwangi B, Cao B, Shott ME, Soares JC, Frank GKW (2018) Cortical thickness patterns as state biomarker of anorexia nervosa. Int J Eat Disord 51:241\u2013249. https:\/\/doi.org\/10.1002\/eat.22828","journal-title":"Int J Eat Disord"},{"key":"1905_CR13","doi-asserted-by":"publisher","unstructured":"Geisler D, Borchardt V, Boehm I, King JA, Tam FI, Marxen M et al (2020) Altered global brain network topology as a trait marker in patients with anorexia nervosa. Psychol Med 50:107\u2013115. https:\/\/doi.org\/10.1017\/S0033291718004002","DOI":"10.1017\/S0033291718004002"},{"key":"1905_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-016-0165-x","volume":"9","author":"Y Guo","year":"2016","unstructured":"Guo Y, Wei Z, Keating BJ, Hakonarson H (2016) Machine learning derived risk prediction of anorexia nervosa. BMC Med Genomics 9:4.\u00a0https:\/\/doi.org\/10.1186\/s12920-016-0165-x","journal-title":"BMC Med Genomics"},{"key":"1905_CR15","doi-asserted-by":"publisher","unstructured":"Burstein D, Griffen TC, Therrien K, Bendl J, Venkatesh S, Dong P et al (2023) Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism. Nat Genet 55:1462\u20131470. https:\/\/doi.org\/10.1038\/s41588-023-01464-1","DOI":"10.1038\/s41588-023-01464-1"},{"key":"1905_CR16","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s40337-025-01265-3","volume":"13","author":"G Brizzi","year":"2025","unstructured":"Brizzi G, Pupillo C, Sajno E, Boltri M, Brusa F, Scarpina F et al (2025) Predicting anorexia nervosa treatment efficacy: an explainable machine learning approach. J Eat Disord 13:97. https:\/\/doi.org\/10.1186\/s40337-025-01265-3","journal-title":"J Eat Disord"},{"key":"1905_CR17","doi-asserted-by":"publisher","unstructured":"Haynos AF, Wang SB, Lipson S, Peterson CB, Mitchell JE, Halmi KA et al (2021) Machine learning enhances prediction of illness course: a longitudinal study in eating disorders. Psychol Med 51:1392\u20131402. https:\/\/doi.org\/10.1017\/S0033291720000227","DOI":"10.1017\/S0033291720000227"},{"key":"1905_CR18","doi-asserted-by":"publisher","unstructured":"Chancellor S, Birnbaum ML, Caine ED, Silenzio VMB, De Choudhury M (2019) A taxonomy of ethical tensions in inferring mental health states from social media. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM, Atlanta, pp 79\u201388. https:\/\/doi.org\/10.1145\/3287560.3287587","DOI":"10.1145\/3287560.3287587"},{"key":"1905_CR19","doi-asserted-by":"publisher","unstructured":"Ben\u00edtez-Andrades JA, Alija-P\u00e9rez JM, Vidal ME, Pastor-Vargas R, Garc\u00eda-Ord\u00e1s MT (2022) Traditional machine learning models and bidirectional encoder representations from transformer (BERT)-based automatic classification of tweets about eating disorders: algorithm development and validation study. JMIR Med Inform 10:e34492. https:\/\/doi.org\/10.2196\/34492","DOI":"10.2196\/34492"},{"key":"1905_CR20","doi-asserted-by":"publisher","unstructured":"Ralph-Nearman C, Sandoval-Araujo LE, Karem A, Cusack CE, Glatt S, Hooper MA et al (2024) Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life. Psychol Med 54:1084\u20131090. https:\/\/doi.org\/10.1017\/S003329172300288X","DOI":"10.1017\/S003329172300288X"},{"key":"1905_CR21","doi-asserted-by":"publisher","unstructured":"Kernberg A, Gold JA, Mohan V (2024) Using ChatGPT-4 to create structured medical notes from audio recordings of physician-patient encounters: comparative study. J Med Internet Res 26:e54419. https:\/\/doi.org\/10.2196\/54419","DOI":"10.2196\/54419"},{"key":"1905_CR22","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1002\/eat.23662","volume":"55","author":"EE Fitzsimmons\u2010Craft","year":"2022","unstructured":"Fitzsimmons\u2010Craft EE, Chan WW, Smith AC, Firebaugh M, Fowler LA, Topooco N et al (2022) Effectiveness of a chatbot for eating disorders prevention: a randomized clinical trial. Int J Eat Disord 55:343\u2013353. https:\/\/doi.org\/10.1002\/eat.23662","journal-title":"Int J Eat Disord"},{"key":"1905_CR23","unstructured":"Jargon J (2023) A chatbot was designed to help prevent eating disorders. Then it gave dieting tips. Wall Street Journal. https:\/\/www.wsj.com\/tech\/ai\/eating-disorder-chatbotai- 2aecb179"},{"key":"1905_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-025-02683-2","author":"J Baeyaert","year":"2025","unstructured":"Baeyaert J (2025) The failure of AI therapy bots: technical illusions and institutional betrayals. AI Soc. https:\/\/doi.org\/10.1007\/s00146-025-02683-2","journal-title":"AI Soc"},{"key":"1905_CR25","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1002\/eat.24579","volume":"59","author":"J Maas","year":"2026","unstructured":"Maas J, Franssen S, Petkovic M, Cardona Cano S, Dingemans AE, Van Oosterzee AM et al (2026) Artificial intelligence in eating disorder treatment: a qualitative analysis of clinical opportunities, barriers, and ethical considerations from multi\u2010disciplinary focus groups. Int J Eat Disord 59:299\u2013310. https:\/\/doi.org\/10.1002\/eat.24579","journal-title":"Int J Eat Disord"},{"key":"1905_CR26","doi-asserted-by":"publisher","unstructured":"Char DS, Shah NH, Magnus D (2018) Implementing machine learning in health care\u2014addressing ethical challenges. N Engl J Med 378:981\u2013983. https:\/\/doi.org\/10.1056\/NEJMp1714229","DOI":"10.1056\/NEJMp1714229"},{"key":"1905_CR27","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366:447\u2013453. https:\/\/doi.org\/10.1126\/science.aax2342","journal-title":"Science"},{"key":"1905_CR28","unstructured":"Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act) (Text with EEA relevance). 2024 Jun 13. http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj"},{"key":"1905_CR29","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1002\/eat.24215","volume":"57","author":"ML Norris","year":"2024","unstructured":"Norris ML, Obeid N, El-Emam K (2024) Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. Int J Eat Disord 57:1357\u20131368. https:\/\/doi.org\/10.1002\/eat.24215","journal-title":"Int J Eat Disord"},{"key":"1905_CR30","doi-asserted-by":"publisher","unstructured":"Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285\u2013296. https:\/\/doi.org\/10.1016\/j.jbusres.2021.04.070","DOI":"10.1016\/j.jbusres.2021.04.070"},{"key":"1905_CR31","doi-asserted-by":"publisher","unstructured":"Aria M, Cuccurullo C (2017) bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr 11:959\u2013975. https:\/\/doi.org\/10.1016\/j.joi.2017.08.007","DOI":"10.1016\/j.joi.2017.08.007"},{"key":"1905_CR32","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B et al (2024) TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385:e078378. https:\/\/doi.org\/10.1136\/bmj-2023-078378","journal-title":"BMJ"},{"key":"1905_CR33","doi-asserted-by":"publisher","first-page":"e537","DOI":"10.1016\/S2589-7500(20)30218-1","volume":"2","author":"X Liu","year":"2020","unstructured":"Liu X,\u00a0Cruz Rivera S, Moher D, Calvert MJ, Denniston AK (2020) Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: the CONSORT-AI extension. Lancet Digit Health 2:e537\u2013e548. https:\/\/doi.org\/10.1016\/S2589-7500(20)30218-1","journal-title":"Lancet Digit Health"},{"key":"1905_CR34","doi-asserted-by":"publisher","first-page":"2777","DOI":"10.1017\/S0033291721004748","volume":"53","author":"LN Forrest","year":"2023","unstructured":"Forrest LN, Ivezaj V, Grilo CM (2023) Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial. Psychol Med 53:2777\u20132788. https:\/\/doi.org\/10.1017\/S0033291721004748","journal-title":"Psychol Med"},{"key":"1905_CR35","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1038\/s41380-021-01161-7","volume":"27","author":"M Solmi","year":"2022","unstructured":"Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G et al (2022) Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry 27:281\u2013295. https:\/\/doi.org\/10.1038\/s41380-021-01161-7","journal-title":"Mol Psychiatry"},{"key":"1905_CR36","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s40519-023-01563-2","volume":"28","author":"S Song","year":"2023","unstructured":"Song S, Stern CM, Deitsch T, Sala M (2023) Acculturation and eating disorders: a systematic review. Eat Weight Disord 28:39. https:\/\/doi.org\/10.1007\/s40519-023-01563-2","journal-title":"Eat Weight Disord"},{"key":"1905_CR37","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1002\/eat.24002","volume":"56","author":"B Philip","year":"2023","unstructured":"Philip B, Poku O, Attoh-Okine ND, Presskreischer R (2023) The need for epidemiological research on eating disorders in Africa and the Caribbean. Int J Eat Disord 56:1688\u20131693. https:\/\/doi.org\/10.1002\/eat.24002","journal-title":"Int J Eat Disord"},{"key":"1905_CR38","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1002\/eat.24543","volume":"58","author":"J Linardon","year":"2025","unstructured":"Linardon J, Thomas JJ, Crow SJ, Ghaderi A, Hilbert A, Klump KL et al (2025) Conducting eating disorder research in the era of generative AI: researcher perspectives and guidelines from the International Journal of Eating Disorders. Int J Eat Disord 58:2306\u20132316. https:\/\/doi.org\/10.1002\/eat.24543","journal-title":"Int J Eat Disord"}],"container-title":["Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40519-026-01905-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40519-026-01905-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40519-026-01905-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T11:20:22Z","timestamp":1792668022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40519-026-01905-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,20]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1905"],"URL":"https:\/\/doi.org\/10.1007\/s40519-026-01905-w","relation":{},"ISSN":["1590-1262"],"issn-type":[{"value":"1590-1262","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,20]]},"assertion":[{"value":"23 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This is a bibliometric study based entirely on published literature. No human participants, biological material, or identifiable data were involved. Ethical approval was therefore not required.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"AI-assisted tools were used solely for language editing to improve readability, grammar, and style. No AI tool was used to generate, analyse, or interpret any scientific content. The authors take full responsibility for the integrity and accuracy of all content and approved the final manuscript.","order":2,"name":"Ethics","label":"AI-assisted writing","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"89"}}