{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T23:28:26Z","timestamp":1793662106263,"version":"4.5.1"},"reference-count":41,"publisher":"Walter de Gruyter GmbH","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence (AI) is rapidly transforming laboratory medicine, impacting medical devices and healthcare practices. Despite these advancements, AI-based medical device software (MDSW) introduces a new layer of complexity in regulatory compliance. This paper outlines the regulatory landscape for MDSW and AI-driven MDSW, clarifying the responsibilities of laboratory professionals and manufacturers under the\n                    <jats:italic>In Vitro<\/jats:italic>\n                    Diagnostic Regulation (IVDR), ISO 15189:2022, and the Artificial Intelligence Act. An analysis of 89 MDSWs approved under the IVDR, derived from the European Database on Medical Devices (EUDAMED) reveals a diverse landscape of applications, ranging from digital pathology and molecular diagnostics to laboratory automation and clinical decision support. While Germany currently dominates the EU market for these devices, and the majority of approved MDSW remain non-AI driven and classified as low-risk, the increasing presence of AI-powered Class C devices underscores the growing potential of software in complex diagnostic scenarios. However, realizing the full potential of AI in laboratory medicine requires careful navigation of the evolving regulatory landscape. Key challenges persist, including defining intended use, ensuring robust clinical evidence, mitigating data bias, and establishing rigorous post-market surveillance. Balancing regulatory oversight with innovation is critical to fostering the development of trustworthy AI systems without stifling progress. As regulatory frameworks continue to evolve, establishing clear validation methodologies and transparent compliance pathways will be essential to unlocking the full potential of AI in laboratory medicine while ensuring the highest standards of safety and clinical effectiveness.\n                  <\/jats:p>","DOI":"10.1515\/cclm-2025-0482","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T04:09:50Z","timestamp":1748318990000},"page":"1891-1914","source":"Crossref","is-referenced-by-count":36,"title":["Regulating the future of laboratory medicine: European regulatory landscape of AI-driven medical device software in laboratory medicine"],"prefix":"10.1515","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5321-9354","authenticated-orcid":false,"given":"Hikmet Can","family":"\u00c7ubuk\u00e7u","sequence":"first","affiliation":[{"name":"Rare Diseases Department , General Directorate of Health Services, Turkish Ministry of Health , Ankara , T\u00fcrkiye"},{"name":"Department of Medical Biochemistry , Sincan Training and Research Hospital , Ankara , T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2903-3135","authenticated-orcid":false,"given":"Guilaine","family":"Boursier","sequence":"additional","affiliation":[{"name":"CHU Montpellier, Department of Molecular Genetics and Cytogenomics , National Reference Laboratory of Autoinflammatory Diseases, French National Reference Center of Autoinflammatory Diseases and Amyloidosis (CeR\u00e9MAIA), IRMB, INSERM U1183, Univ Montpellier , Montpellier , France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3729-771X","authenticated-orcid":false,"given":"Solveig","family":"Linko","sequence":"additional","affiliation":[{"name":"Linko Q-Solutions , Helsinki , Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7104-0200","authenticated-orcid":false,"given":"Francisco A.","family":"Bernabeu-Andreu","sequence":"additional","affiliation":[{"name":"Servicio Bioqu\u00edmica - An\u00e1lisis Cl\u00ednicos Hospital Universitario Puerta de Hierro , Madrid , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4945-6637","authenticated-orcid":false,"given":"Pika","family":"Me\u0161ko Brguljan","sequence":"additional","affiliation":[{"name":"Department of Clinical Chemistry , University Clinic for Respiratory and Allergic Diseases , Golnik , Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7636-4631","authenticated-orcid":false,"given":"Katerina","family":"Tosheska-Trajkovska","sequence":"additional","affiliation":[{"name":"Department of Medical and Experimental Biochemistry, Faculty of Medicine , University Ss.\u201dKiril and Metodij\u201d , Skopje , North Macedonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9420-9961","authenticated-orcid":false,"given":"Duilio","family":"Brugnoni","sequence":"additional","affiliation":[{"name":"Central Laboratory for Clinical Chemistry, Spedali Civili , Brescia , Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2641-9817","authenticated-orcid":false,"given":"Neda","family":"Milinkovic","sequence":"additional","affiliation":[{"name":"Department of Medical Biochemistry, Faculty of Pharmacy , University of Belgrade , Belgrade , Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-7885","authenticated-orcid":false,"given":"Andrea","family":"Padoan","sequence":"additional","affiliation":[{"name":"Department of Medicine (DIMED) , University of Padova and University Hospital of Padova , Padova , Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1771-669X","authenticated-orcid":false,"given":"Marc","family":"Thelen","sequence":"additional","affiliation":[{"name":"SKML , Nijmegen , The Netherlands"},{"name":"Department of Laboratory Medicine , Radboud University Medical Center , Nijmegen , The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2025,5,28]]},"reference":[{"key":"2026022607582682294_j_cclm-2025-0482_ref_001","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7u, HC, Topcu, D, Yenice, S. Machine learning-based clinical decision support using laboratory data. Clin Chem Lab Med 2024;62:793\u2013823. https:\/\/doi.org\/10.1515\/cclm-2023-1037.","DOI":"10.1515\/cclm-2023-1037"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_002","doi-asserted-by":"crossref","unstructured":"Vasey, B, Ursprung, S, Beddoe, B, Taylor, EH, Marlow, N, Bilbro, N, et al.. Association of clinician diagnostic performance with machine learning-based decision support systems: a systematic review. JAMA Netw Open 2021;4:e211276. https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.1276.","DOI":"10.1001\/jamanetworkopen.2021.1276"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_003","doi-asserted-by":"crossref","unstructured":"Gungoren, MS. Crossing the chasm: strategies for digital transformation in clinical laboratories. Clin Chem Lab Med 2023;61:570\u20135. https:\/\/doi.org\/10.1515\/cclm-2022-1229.","DOI":"10.1515\/cclm-2022-1229"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_004","doi-asserted-by":"crossref","unstructured":"Yu, M, Bazydlo, LAL, Bruns, DE, Harrison, JHJr. Streamlining quality review of mass spectrometry data in the clinical laboratory by use of machine learning. Arch Pathol Lab Med 2019;143:990\u20138. https:\/\/doi.org\/10.5858\/arpa.2018-0238-oa.","DOI":"10.5858\/arpa.2018-0238-OA"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_005","doi-asserted-by":"crossref","unstructured":"Fang, K, Dong, Z, Chen, X, Zhu, J, Zhang, B, You, J, et al.. Using machine learning to identify clotted specimens in coagulation testing. Clin Chem Lab Med 2021;59:1289\u201397. https:\/\/doi.org\/10.1515\/cclm-2021-0081.","DOI":"10.1515\/cclm-2021-0081"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_006","doi-asserted-by":"crossref","unstructured":"Mitani, T, Doi, S, Yokota, S, Imai, T, Ohe, K. Highly accurate and explainable detection of specimen mix-up using a machine learning model. Clin Chem Lab Med 2020;58:375\u201383. https:\/\/doi.org\/10.1515\/cclm-2019-0534.","DOI":"10.1515\/cclm-2019-0534"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_007","doi-asserted-by":"crossref","unstructured":"Durant, TJS, Olson, EM, Schulz, WL, Torres, R. Very deep convolutional neural networks for morphologic classification of erythrocytes. Clin Chem 2017;63:1847\u201355. https:\/\/doi.org\/10.1373\/clinchem.2017.276345.","DOI":"10.1373\/clinchem.2017.276345"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_008","doi-asserted-by":"crossref","unstructured":"Chabrun, F, Lacombe, V, Dieu, X, Genevi\u00e8ve, F, Urbanski, G. Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears. Clin Chem Lab Med 2023;61:1275\u20139. https:\/\/doi.org\/10.1515\/cclm-2022-1283.","DOI":"10.1515\/cclm-2022-1283"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_009","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7u, HC. Performance evaluation of internal quality control rules, EWMA, CUSUM, and the novel machine learning model. Turk J Biochem 2021;46:661\u201370. https:\/\/doi.org\/10.1515\/tjb-2021-0199.","DOI":"10.1515\/tjb-2021-0199"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_010","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7u, HC, Topcu, D, Bayraktar, N, G\u00fcl\u015fen, M, Sar\u0131, N, Arslan, AH. Detection of COVID-19 by machine learning using routine laboratory tests. Am J Clin Pathol 2022;157:758\u201366. https:\/\/doi.org\/10.1093\/ajcp\/aqab187.","DOI":"10.1093\/ajcp\/aqab187"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_011","doi-asserted-by":"crossref","unstructured":"Zucca, S, Nicora, G, De Paoli, F, Carta, MG, Bellazzi, R, Magni, P, et al.. An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases. Hum Genet 2024;144:159\u201371. https:\/\/doi.org\/10.1007\/s00439-023-02638-x.","DOI":"10.1007\/s00439-023-02638-x"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_012","doi-asserted-by":"crossref","unstructured":"Wilkes, EH, Rumsby, G, Woodward, GM. Using machine learning to aid the interpretation of urine steroid profiles. Clin Chem 2018;64:1586\u201395. https:\/\/doi.org\/10.1373\/clinchem.2018.292201.","DOI":"10.1373\/clinchem.2018.292201"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_013","doi-asserted-by":"crossref","unstructured":"\u00c7ubuk\u00e7u, HC, Topcu, D. Estimation of low-density lipoprotein cholesterol concentration using machine learning. Lab Med 2022;53:161\u201371. https:\/\/doi.org\/10.1093\/labmed\/lmab065.","DOI":"10.1093\/labmed\/lmab065"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_028","unstructured":"The European Parliament and the Council of The European Union. Regulation (EU) 2024\/1689 of the European Parliament and of the Council (Artificial Intelligence Act); 2024."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_014","doi-asserted-by":"crossref","unstructured":"Muralidharan, V, Adewale, BA, Huang, CJ, Nta, MT, Ademiju, PO, Pathmarajah, P, et al.. A scoping review of reporting gaps in FDA-approved AI medical devices. NPJ Digit Med 2024;7:273. https:\/\/doi.org\/10.1038\/s41746-024-01270-x.","DOI":"10.1038\/s41746-024-01270-x"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_015","doi-asserted-by":"crossref","unstructured":"Bajwa, J, Munir, U, Nori, A, Williams, B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J 2021;8:e188\u201394. https:\/\/doi.org\/10.7861\/fhj.2021-0095.","DOI":"10.7861\/fhj.2021-0095"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_016","doi-asserted-by":"crossref","unstructured":"Mohsin Khan, M, Shah, N, Shaikh, N, Thabet, A, Alrabayah, T, Belkhair, S. Towards secure and trusted AI in healthcare: a systematic review of emerging innovations and ethical challenges. Int J Med Inform 2024;195:105780. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105780.","DOI":"10.1016\/j.ijmedinf.2024.105780"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_017","doi-asserted-by":"crossref","unstructured":"Muehlematter, UJ, Daniore, P, Vokinger, KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015\u201320): a comparative analysis. Lancet Digit Health 2021;3:e195\u2013203. https:\/\/doi.org\/10.1016\/s2589-7500(20)30292-2.","DOI":"10.1016\/S2589-7500(20)30292-2"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_018","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Rodr\u00edguez, N, Del Ser, J, Coeckelbergh, M, L\u00f3pez de Prado, M, Herrera-Viedma, E, Herrera, F. Connecting the dots in trustworthy Artificial Intelligence: from AI principles, ethics, and key requirements to responsible AI systems and regulation. Inf Fusion 2023;99:101896. https:\/\/doi.org\/10.1016\/j.inffus.2023.101896.","DOI":"10.1016\/j.inffus.2023.101896"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_019","unstructured":"Software and AI as a medical device change programme - roadmap; 2023. Available from: https:\/\/www.gov.uk\/government\/publications\/software-and-ai-as-a-medical-device-change-programme\/software-and-ai-as-a-medical-device-change-programme-roadmap [Accessed 9 February 2025]."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_020","doi-asserted-by":"crossref","unstructured":"Mennella, C, Maniscalco, U, De Pietro, G, Esposito, M. Ethical and regulatory challenges of AI technologies in healthcare: a narrative review. Heliyon 2024;10:e26297. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e26297.","DOI":"10.1016\/j.heliyon.2024.e26297"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_021","unstructured":"Software as a medical device (SaMD): key definitions; 2013. Available from: https:\/\/www.imdrf.org\/sites\/default\/files\/docs\/imdrf\/final\/technical\/imdrf-tech-131209-samd-key-definitions-140901.pdf [Accessed 9 February 2025]."},{"key":"#cr-split#-2026022607582682294_j_cclm-2025-0482_ref_022.1","unstructured":"MDCG 2019-11: guidance on qualification and classification of software in regulation (EU) 2017\/745\u00a0-\u00a0MDR and regulation (EU) 2017\/746\u00a0-\u00a0IVDR"},{"key":"#cr-split#-2026022607582682294_j_cclm-2025-0482_ref_022.2","unstructured":"Available from: https:\/\/health.ec.europa.eu\/system\/files\/2020-09\/md_mdcg_2019_11_guidance_qualification_classification_software_en_0.pdf [Accessed 9 February 2019]."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_023","unstructured":"International organization for standardization technical committee: ISO\/TC 212. ISO 15189:2022 Medical laboratories \u2014 requirements for quality and competence, 4th ed. Geneva, Switzerland: ISO; 2022."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_024","unstructured":"The European Parliament and the Council of The European Union. Regulation (EU) 2017\/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices. Official Journal of the European Union 2017."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_025","doi-asserted-by":"crossref","unstructured":"Kanti, SPY, Cs\u00f3ka, I, Adalbert, L, J\u00f3j\u00e1rt-Laczkovich, O. Analysis of the renewed European Medical Device Regulations in the frame of the non - EU regulatory landscape during the COVID facilitated change. J\u00a0Pharm Sci 2022;111:2674\u201386. https:\/\/doi.org\/10.1016\/j.xphs.2022.07.011.","DOI":"10.1016\/j.xphs.2022.07.011"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_026","unstructured":"Bodies notified under EU legislation. Available from: https:\/\/webgate.ec.europa.eu\/single-market-compliance-space\/notified-bodies\/by-country [Accessed 2025]."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_027","unstructured":"EUDAMED - European database on medical devices; 2025. Available from: https:\/\/ec.europa.eu\/tools\/eudamed\/#\/screen\/search-device?applicableLegislation=refdata.applicable-legislation.ivdr&deviceTypes=refdata.special-ivd-device-type.software deviceStatusCode=refdata.device-model-status.on-the-market&submitted=true. 2025."},{"key":"2026022607582682294_j_cclm-2025-0482_ref_029","doi-asserted-by":"crossref","unstructured":"Aboy, M, Minssen, T, Vayena, E. Navigating the EU AI Act: implications for regulated digital medical products. NPJ Digit Med 2024;7:237. https:\/\/doi.org\/10.1038\/s41746-024-01232-3.","DOI":"10.1038\/s41746-024-01232-3"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_030","doi-asserted-by":"crossref","unstructured":"Spies, NC, Farnsworth, CW, Wheeler, S, McCudden, CR. Validating, implementing, and monitoring machine learning solutions in the clinical laboratory safely and effectively. Clin Chem 2024;70:1334\u201343. https:\/\/doi.org\/10.1093\/clinchem\/hvae126.","DOI":"10.1093\/clinchem\/hvae126"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_031","doi-asserted-by":"crossref","unstructured":"Miller, WG, Greenberg, N. Harmonization and standardization: where are we now? J\u00a0Appl Lab Med 2021;6:510\u201321. https:\/\/doi.org\/10.1093\/jalm\/jfaa189.","DOI":"10.1093\/jalm\/jfaa189"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_032","doi-asserted-by":"crossref","unstructured":"Cavalier, E, Vasikaran, S, Bhattoa, HP, Heijboer, AC, Makris, K, Ulmer, CZ. The path to the standardization of PTH: is this a realistic possibility? a position paper of the IFCC C-BM. Clin Chim Acta 2021;515:44\u201351. https:\/\/doi.org\/10.1016\/j.cca.2020.12.022.","DOI":"10.1016\/j.cca.2020.12.022"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_033","doi-asserted-by":"crossref","unstructured":"Wieringa, GE, Sturgeon, CM, Trainer, PJ. The harmonisation of growth hormone measurements: taking the next steps. Clin Chim Acta 2014;432:68\u201371. https:\/\/doi.org\/10.1016\/j.cca.2014.01.014.","DOI":"10.1016\/j.cca.2014.01.014"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_034","doi-asserted-by":"crossref","unstructured":"Braga, F, Pasqualetti, S, Frusciante, E, Borrillo, F, Chibireva, M, Panteghini, M. Harmonization status of serum ferritin measurements and implications for use as marker of iron-related disorders. Clin Chem 2022;68:1202\u201310. https:\/\/doi.org\/10.1093\/clinchem\/hvac099.","DOI":"10.1093\/clinchem\/hvac099"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_035","doi-asserted-by":"crossref","unstructured":"van Schrojenstein Lantman, M, van de Logt, AE, Prudon-Rosmulder, E, Langelaan, M, Demir, AY, Kurstjens, S, et al.. Albumin determined by bromocresol green leads to erroneous results in routine evaluation of patients with chronic kidney disease. Clin Chem Lab Med 2023;61:2167\u201377. https:\/\/doi.org\/10.1515\/cclm-2023-0463.","DOI":"10.1515\/cclm-2023-0463"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_036","doi-asserted-by":"crossref","unstructured":"Jassam, N, Weykamp, C, Thomas, A, Secchiero, S, Sciacovelli, L, Plebani, M, et al.. Post-standardization of routine creatinine assays: are they suitable for clinical applications. Ann Clin Biochem 2017;54:386\u201394. https:\/\/doi.org\/10.1177\/0004563216664541.","DOI":"10.1177\/0004563216664541"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_037","doi-asserted-by":"crossref","unstructured":"Loh, TP, Cooke, BR, Tran, TCM, Markus, C, Zakaria, R, Ho, CS, et al.. The LEAP checklist for laboratory evaluation and analytical performance characteristics reporting of clinical measurement procedures. Biochem Med 2023;33:030505. https:\/\/doi.org\/10.11613\/BM.2023.030505.","DOI":"10.11613\/BM.2023.030505"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_038","doi-asserted-by":"crossref","unstructured":"Niemiec, E. Will the EU Medical Device Regulation help to improve the safety and performance of medical AI devices? Digit Health 2022;8:20552076221089079. https:\/\/doi.org\/10.1177\/20552076221089079.","DOI":"10.1177\/20552076221089079"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_039","doi-asserted-by":"crossref","unstructured":"Wu, E, Wu, K, Daneshjou, R, Ouyang, D, Ho, DE, Zou, J. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med 2021;27:582\u20134. https:\/\/doi.org\/10.1038\/s41591-021-01312-x.","DOI":"10.1038\/s41591-021-01312-x"},{"key":"2026022607582682294_j_cclm-2025-0482_ref_040","doi-asserted-by":"crossref","unstructured":"Gilbert, S, Anderson, S, Daumer, M, Li, P, Melvin, T, Williams, R. Learning from experience and finding the right balance in the governance of artificial intelligence and digital health technologies. J\u00a0Med Internet Res 2023;25:e43682. https:\/\/doi.org\/10.2196\/43682.","DOI":"10.2196\/43682"}],"container-title":["Clinical Chemistry and Laboratory Medicine (CCLM)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/cclm-2025-0482\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/cclm-2025-0482\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T07:58:44Z","timestamp":1772092724000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/cclm-2025-0482\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,28]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,5,28]]},"published-print":{"date-parts":[[2025,9,25]]}},"alternative-id":["10.1515\/cclm-2025-0482"],"URL":"https:\/\/doi.org\/10.1515\/cclm-2025-0482","relation":{},"ISSN":["1434-6621","1437-4331"],"issn-type":[{"value":"1434-6621","type":"print"},{"value":"1437-4331","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,28]]}}}