{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:34:37Z","timestamp":1776134077187,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician\u2019s trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached. This review first defined the concepts underlying XAI, establishing the tools available and how they can benefit digestive healthcare. Examples of the application of XAI in digestive healthcare were provided, and potential future uses were proposed. In addition, aspects of the regulatory frameworks that must be established and the ethical concerns that must be borne in mind during the development of these tools were discussed. Finally, we considered the challenges that this technology faces to ensure that optimal benefits are reaped, highlighting the need for more research into the use of XAI in this field.<\/jats:p>","DOI":"10.3390\/jcm14020549","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T06:46:10Z","timestamp":1737009970000},"page":"549","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-0830","authenticated-orcid":false,"given":"Miguel","family":"Mascarenhas","sequence":"first","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"},{"name":"CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-7049","authenticated-orcid":false,"given":"Francisco","family":"Mendes","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0484-4804","authenticated-orcid":false,"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]},{"given":"Tiago","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Afonso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]},{"given":"Pedro","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4099-002 Porto, Portugal"},{"name":"Digestive Artificial Intelligence Development, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0887-8796","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Fonseca","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"},{"name":"CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200 427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9387-9872","authenticated-orcid":false,"given":"Guilherme","family":"Macedo","sequence":"additional","affiliation":[{"name":"Precision Medicine Unit, Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"},{"name":"Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The Potential for Artificial Intelligence in Healthcare","volume":"6","author":"Davenport","year":"2019","journal-title":"Future Healthc. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Afonso, J., Ribeiro, T., Andrade, P., Cardoso, H., and Macedo, G. (2023). The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. Medicina, 59.","DOI":"10.3390\/medicina59040790"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-3-030-16391-4_11","article-title":"Artificial Intelligence and Personalized Medicine","volume":"Volume 178","author":"Han","year":"2019","journal-title":"Precision Medicine in Cancer Therapy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.drudis.2020.10.010","article-title":"Artificial Intelligence in Drug Discovery and Development","volume":"26","author":"Paul","year":"2021","journal-title":"Drug Discov. Today"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/s41586-023-05905-z","article-title":"Computational Approaches Streamlining Drug Discovery","volume":"616","author":"Sadybekov","year":"2023","journal-title":"Nature"},{"key":"ref_6","first-page":"1","article-title":"Why Are We Using Black Box Models in AI When We Don\u2019t Need To? A Lesson from An Explainable AI Competition","volume":"1","author":"Rudin","year":"2019","journal-title":"Harv. Data Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.imed.2023.08.001","article-title":"Medical Artificial Intelligence and the Black Box Problem: A View Based on the Ethical Principle of \u201cDo No Harm\u201d","volume":"4","author":"Xu","year":"2024","journal-title":"Intell. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"652","DOI":"10.3390\/ai4030034","article-title":"Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare","volume":"4","author":"Hulsen","year":"2023","journal-title":"AI"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.inffus.2021.07.016","article-title":"Unbox the Black-Box for the Medical Explainable AI via Multi-Modal and Multi-Centre Data Fusion: A Mini-Review, Two Showcases and Beyond","volume":"77","author":"Yang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_10","unstructured":"(2025, January 01). The Royal Society Explainable AI: The Basics. Available online: https:\/\/royalsociety.org\/-\/media\/policy\/projects\/explainable-ai\/AI-and-interpretability-policy-briefing.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109370","DOI":"10.1016\/j.compeleceng.2024.109370","article-title":"A Review of Explainable Artificial Intelligence in Healthcare","volume":"118","author":"Sadeghi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23.","DOI":"10.3390\/e23010018"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"van der Zander, Q.E.W., van der Ende-van Loon, M.C.M., Janssen, J.M.M., Winkens, B., Van Der Sommen, F., Masclee, A.A.M., and Schoon, E.J. (2022). Artificial Intelligence in (Gastrointestinal) Healthcare: Patients\u2019 and Physicians\u2019 Perspectives. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-20958-2"},{"key":"ref_14","unstructured":"Mascarenhas, M., Cardoso, H., and Macedo, G. (2023). Artificial Intelligence in Capsule Endoscopy: A Gamechanger for a Groundbreaking Technique, Academic Press."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.3748\/wjg.v28.i20.2152","article-title":"Artificial Intelligence: Emerging Player in the Diagnosis and Treatment of Digestive Disease","volume":"28","author":"Chen","year":"2022","journal-title":"World J. Gastroenterol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Singh, S., and Guestrin, C. (2016, January 12\u201317). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref_17","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kuhn, H.W., and Tucker, A.W. (1953). 17. A Value for n-Person Games. Contributions to the Theory of Games (AM-28), Volume II, Princeton University Press.","DOI":"10.1515\/9781400881970"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Biecek, P., and Burzykowski, T. (2021). Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models, CRC Press. [1st ed.]. Chapman & Hall\/CRC Data Science Series.","DOI":"10.1201\/9780429027192"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1111\/rssb.12377","article-title":"Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models","volume":"82","author":"Apley","year":"2020","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable Artificial Intelligence (XAI) in Deep Learning-Based Medical Image Analysis","volume":"79","author":"Kuijf","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s00464-022-09470-w","article-title":"Real-Time Artificial Intelligence (AI)-Aided Endoscopy Improves Adenoma Detection Rates Even in Experienced Endoscopists: A Cohort Study in Singapore","volume":"37","author":"Koh","year":"2023","journal-title":"Surg. Endosc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.2169\/internalmedicine.6446-20","article-title":"The Differential Diagnosis of Colorectal Polyps Using Colon Capsule Endoscopy","volume":"60","author":"Nakazawa","year":"2021","journal-title":"Intern. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10620-021-06830-9","article-title":"Use of Artificial Intelligence in the Prediction of Malignant Potential of Gastric Gastrointestinal Stromal Tumors","volume":"67","author":"Seven","year":"2022","journal-title":"Dig. Dis. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.3748\/wjg.v27.i24.3543","article-title":"Usefulness of Artificial Intelligence in Gastric Neoplasms","volume":"27","author":"Kim","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2020.04.157","article-title":"Convolutional Neural Networks for Medical Image Analysis: State-of-the-Art, Comparisons, Improvement and Perspectives","volume":"444","author":"Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_27","first-page":"200275","article-title":"Skin Cancer Classification Using Explainable Artificial Intelligence on Pre-Extracted Image Features","volume":"20","author":"Khater","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Binzagr, F. (2024). Explainable AI-Driven Model for Gastrointestinal Cancer Classification. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1349373"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1007\/s00535-023-02025-3","article-title":"Exploring the Challenge of Early Gastric Cancer Diagnostic AI System Face in Multiple Centers and Its Potential Solutions","volume":"58","author":"Dong","year":"2023","journal-title":"J. Gastroenterol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Afonso, J., Ribeiro, T., Cardoso, H., Andrade, P., Ferreira, J.P.S., Saraiva, M.M., and Macedo, G. (2022). Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061445"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"E1264","DOI":"10.1055\/a-1490-8960","article-title":"Artificial Intelligence and Colon Capsule Endoscopy: Automatic Detection of Blood in Colon Capsule Endoscopy Using a Convolutional Neural Network","volume":"09","author":"Ferreira","year":"2021","journal-title":"Endosc. Int. Open"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1053\/j.gastro.2020.04.062","article-title":"Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial","volume":"159","author":"Repici","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1053\/j.gastro.2022.05.028","article-title":"Computer-Aided Detection Improves Adenomas per Colonoscopy for Screening and Surveillance Colonoscopy: A Randomized Trial","volume":"163","author":"Shaukat","year":"2022","journal-title":"Gastroenterology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5911","DOI":"10.3748\/wjg.v26.i39.5911","article-title":"Use of Artificial Intelligence in Improving Adenoma Detection Rate during Colonoscopy: Might Both Endoscopists and Pathologists Be Further Helped","volume":"26","author":"Sinagra","year":"2020","journal-title":"World J. Gastroenterol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.1038\/s41467-024-46174-2","article-title":"Enabling Large-Scale Screening of Barrett\u2019s Esophagus Using Weakly Supervised Deep Learning in Histopathology","volume":"15","author":"Bouzid","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Song, Y., Mao, X., Zhou, X., He, S., Chen, Y., Zhang, L., Xu, S., Yan, L., Tang, S., and Ye, L. (2021). Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy. Front. Med., 8.","DOI":"10.3389\/fmed.2021.709347"},{"key":"ref_37","first-page":"820","article-title":"Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Vascular Lesions Using a Convolutional Neural Network","volume":"34","author":"Ribeiro","year":"2021","journal-title":"Ann. Gastroenterol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Saraiva, M.M., Ribeiro, T., Gonz\u00e1lez-Haba, M., Agudo Castillo, B., Ferreira, J.P.S., Vilas Boas, F., Afonso, J., Mendes, F., Martins, M., and Cardoso, P. (2023). Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study. Cancers, 15.","DOI":"10.3390\/cancers15194827"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cosma, G., Bugby, S., Finke, A., and Watkins, J. (2023, January 5\u20138). Morphological Image Analysis and Feature Extraction for Reasoning with AI-Based Defect Detection and Classification Models. Proceedings of the 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico.","DOI":"10.1109\/SSCI52147.2023.10371832"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1053\/j.scrs.2017.04.009","article-title":"High-Resolution Anoscopy: Is It Necessary in the Management of Anal Epithelial Neoplasia","volume":"28","author":"Brady","year":"2017","journal-title":"Semin. Colon Rectal Surg."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Saraiva, M.M., Spindler, L., Manzione, T., Ribeiro, T., Fathallah, N., Martins, M., Cardoso, P., Mendes, F., Fernandes, J., and Ferreira, J. (2024). Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors\u2014A Multicentric Study. Cancers, 16.","DOI":"10.3390\/cancers16101909"},{"key":"ref_42","unstructured":"Brown, T. (2012). Evidence-Based Clinical Reasonsing in Medicine, PMPH-USA, Ltd."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"El-Sappagh, S., Alonso, J.M., Islam, S.M.R., Sultan, A.M., and Kwak, K.S. (2021). A Multilayer Multimodal Detection and Prediction Model Based on Explainable Artificial Intelligence for Alzheimer\u2019s Disease. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-82098-3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6794","DOI":"10.3748\/wjg.v27.i40.6794","article-title":"Artificial Intelligence in Gastroenterology: A State-of-the-Art Review","volume":"27","author":"Engels","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1038\/s42256-022-00544-x","article-title":"AI Model Transferability in Healthcare: A Sociotechnical Perspective","volume":"4","author":"Wiesenfeld","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_46","unstructured":"(2025, January 01). IBM Policy Lab Bias in AI: How We Build Fair AI Systems and Less-Biased Humans 2018. Available online: https:\/\/www.ibm.com\/policy\/bias-in-ai\/."},{"key":"ref_47","first-page":"353","article-title":"Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies","volume":"29","author":"Scherer","year":"2015","journal-title":"SSRN J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.procs.2020.03.078","article-title":"Predictive and Prescriptive Analytics in Healthcare: A Survey","volume":"170","author":"Lopes","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"105142","DOI":"10.1016\/j.ijmedinf.2023.105142","article-title":"Deep Learning-Based Prediction Model for Diagnosing Gastrointestinal Diseases Using Endoscopy Images","volume":"177","author":"Sharma","year":"2023","journal-title":"Int. J. Med. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102301","DOI":"10.1016\/j.inffus.2024.102301","article-title":"Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions","volume":"106","author":"Longo","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pellegrino, R., Federico, A., and Gravina, A.G. (2024). Conversational Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis. Diagnostics, 14.","DOI":"10.3390\/diagnostics14222537"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"17562848241227031","DOI":"10.1177\/17562848241227031","article-title":"Large language models: A primer and gastroenterology applications","volume":"17","author":"Shahab","year":"2024","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.21037\/qims-23-892","article-title":"The role of large language models in medical image processing: A narrative review","volume":"14","author":"Tian","year":"2024","journal-title":"Quant. Imaging Med. Surg."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/2\/549\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:29:57Z","timestamp":1759919397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/2\/549"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,16]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["jcm14020549"],"URL":"https:\/\/doi.org\/10.3390\/jcm14020549","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,16]]}}}