{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T02:50:44Z","timestamp":1781751044499,"version":"3.54.5"},"reference-count":25,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["875171"],"award-info":[{"award-number":["875171"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model-driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific languages (DSLs), and automated transformations to move from high-level specifications to running software. At its core is the Medical Interoperability Language (MILA), a graphical DSL that enables clinicians and data scientists to define queries and machine learning pipelines using shared ontologies. When combined with a federated learning architecture, MILA allows institutions to collaborate without exchanging raw patient data, ensuring semantic consistency across sites while preserving privacy. We evaluate this approach in a multi-center cancer immunotherapy study. The generated pipelines delivered strong predictive performance, with best-performing models achieving up to 98.5% accuracy on selected prediction tasks, while substantially reducing manual coding effort. These findings suggest that MDE principles\u2014metamodeling, semantic integration, and automated code generation\u2014can provide a practical path toward interoperable, reproducible, and reliable digital health platforms.<\/jats:p>","DOI":"10.3390\/informatics13020032","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:45:36Z","timestamp":1770831936000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4549-7366","authenticated-orcid":false,"given":"Mira","family":"Raheem","sequence":"first","affiliation":[{"name":"Faculty of Computers & Artificial Intelligence, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0690-4273","authenticated-orcid":false,"given":"Neamat","family":"Eltazi","sequence":"additional","affiliation":[{"name":"Faculty of Computers & Artificial Intelligence, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Papazoglou","sequence":"additional","affiliation":[{"name":"Scientific Academy for Service Technology e.V. (ServTech), 14482 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-2667","authenticated-orcid":false,"given":"Bernd","family":"Kr\u00e4mer","sequence":"additional","affiliation":[{"name":"Scientific Academy for Service Technology e.V. (ServTech), 14482 Potsdam, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amal","family":"Elgammal","sequence":"additional","affiliation":[{"name":"Faculty of Computers & Artificial Intelligence, Cairo University, Giza 12613, Egypt"},{"name":"Scientific Academy for Service Technology e.V. (ServTech), 14482 Potsdam, Germany"},{"name":"Faculty of Computing and Information Sciences, Egypt University of Informatics, New Administrative Capital, Cairo 11835, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"ref_1","first-page":"77","article-title":"Open problems in medical federated learning","volume":"18","author":"Yoo","year":"2022","journal-title":"Int. J. Web Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abbas, S.R., Abbas, Z., Zahir, A., and Lee, S.W. (2024). Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration. Healthcare, 12.","DOI":"10.3390\/healthcare12242587"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Steffen, B. (2025). Bridging the Gap Between AI and Reality, Springer Nature.","DOI":"10.1007\/978-3-031-75434-0"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10270-024-01211-y","article-title":"Bridging MDE and AI: A systematic review of domain-specific languages and model-driven practices in AI software systems engineering","volume":"24","author":"Berardinelli","year":"2025","journal-title":"Softw. Syst. Model."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107423","DOI":"10.1016\/j.infsof.2024.107423","article-title":"Model driven engineering for machine learning components: A systematic literature review","volume":"169","author":"Naveed","year":"2024","journal-title":"Inf. Softw. Technol."},{"key":"ref_6","first-page":"245","article-title":"Model Driven Development for AI-Based Healthcare Systems: A Review","volume":"Volume 14129 LNCS","author":"Brandon","year":"2025","journal-title":"International Conference on Bridging the Gap between AI and Reality; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e45209","DOI":"10.2196\/45209","article-title":"Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review","volume":"26","author":"Amar","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e53535","DOI":"10.2196\/53535","article-title":"Semantic Interoperability of Electronic Health Records: Systematic Review of Alternative Approaches for Enhancing Patient Information Availability","volume":"12","author":"Palojoki","year":"2024","journal-title":"JMIR Med. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bossenko, I., Randmaa, R., Piho, G., and Ross, P. (2024). Interoperability of health data using FHIR Mapping Language: Transforming HL7 CDA to FHIR with reusable visual components. Front. Digit. Health, 6.","DOI":"10.3389\/fdgth.2024.1480600"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112093","DOI":"10.1016\/j.jss.2024.112093","article-title":"Semantic interoperability for an AI-based applications platform for smart hospitals using HL7 FHIR","volume":"215","author":"Rigas","year":"2024","journal-title":"J. Syst. Softw."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Crowson, M.G., Moukheiber, D., Ar\u00e9valo, A.R., Lam, B.D., Mantena, S., Rana, A., Goss, D., Bates, D.W., and Celi, L.A. (2022). A systematic review of federated learning applications for biomedical data. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000033"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e23728","DOI":"10.2196\/23728","article-title":"Learning from others without sacrificing privacy: Simulation comparing centralized and federated machine learning on mobile health data","volume":"9","author":"Liu","year":"2021","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501813","article-title":"Federated Learning for Healthcare: Systematic Review and Architecture Proposal","volume":"13","author":"Antunes","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_14","unstructured":"Mammen, P.M. (2021). Federated Learning: Opportunities and Challenges. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101419","DOI":"10.1016\/j.xcrm.2024.101419","article-title":"Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture","volume":"5","author":"Teo","year":"2024","journal-title":"Cell Rep. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kalokyri, V., Tachos, N.S., Kalantzopoulos, C.N., Sfakianakis, S., Kondylakis, H., Zaridis, D.I., Colantonio, S., Regge, D., Papanikolaou, N., and The ProCAncer-I consortium (2025). AI Model Passport: Data and System Traceability Framework for Transparent AI in Health. arXiv.","DOI":"10.1016\/j.csbj.2025.09.041"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Namli, T., An\u0131l S\u0131nac\u0131, A., G\u00f6n\u00fcl, S., Herguido, C.R., Garcia-Canadilla, P., Mu\u00f1oz, A.M., Esteve, A.V., and Ert\u00fcrkmen, G.B.L. (2024). A scalable and transparent data pipeline for AI-enabled health data ecosystems. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1393123"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Au Yeung, J., Shek, A., Searle, T., Kraljevic, Z., Dinu, V., Ratas, M., Al-Agil, M., Foy, A., Rafferty, B., and Oliynyk, V. (2024). Natural language processing data services for healthcare providers. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02713-x"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gruendner, J., Schwachhofer, T., Sippl, P., Wolf, N., Erpenbeck, M., Gulden, C., Kapsner, L.A., Zierk, J., Mate, S., and St\u00fcrzl, M. (2019). KETOS: Clinical decision support and machine learning as a service\u2014A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0225442"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ayaz, M., Pasha, M.F., Alahmadi, T.J., Abdullah, N.N.B., and Alkahtani, H.K. (2023). Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data. Healthcare, 11.","DOI":"10.3390\/healthcare11121729"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"McMurry, A.J., Gottlieb, D.I., Miller, T.A., Jones, J.R., Atreja, A., Crago, J., Desai, P.M., Dixon, B.E., Garber, M., and Ignatov, V. (2024). Cumulus: A federated EHR-based learning system powered by FHIR and AI. medRxiv.","DOI":"10.1101\/2024.02.02.24301940"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lehmann, J., Wimberg, G., Autexier, S., Acebes, A., Kalloniatis, C., Lamprinoudakis, C., Giannakopoulos, T., Menegatos, A., Delvinioti, A., and Pagliari, G. (2025). Federated Learning in Multi-Center, Personalized Healthcare for COPD and Comorbidities: The RE-SAMPLE Platform. BIOSTEC (2): HEALTHINF, Science and Technology Publications.","DOI":"10.5220\/0013149800003911"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.csbj.2024.02.014","article-title":"Privacy-preserving federated machine learning on FAIR health data: A real-world application","volume":"24","author":"Sinaci","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_24","unstructured":"Elgammal, A., Kr\u00e4mer, B.J., Papazoglou, M.P., and Raheem, M. (2025). A Semantic Framework for Patient Digital Twins in Chronic Care. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e069090","DOI":"10.1136\/bmjopen-2022-069090","article-title":"Monitoring multidimensional aspects of quality of life after cancer immunotherapy: Protocol for the international multicentre, observational QUALITOP cohort study","volume":"13","author":"Vinke","year":"2023","journal-title":"BMJ Open"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/2\/32\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:23:05Z","timestamp":1771046585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/13\/2\/32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":25,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["informatics13020032"],"URL":"https:\/\/doi.org\/10.3390\/informatics13020032","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,11]]}}}