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Traditional agile frameworks often face friction when aligning the exploratory cycles of model training with fixed sprint cadences. This paper presents a case study on the development of a mobile application for diabetes patient support, powered by a specialized Large Language Model (LLM). The project adapted Scrum ceremonies to manage the full AI lifecycle, from corpus preparation to clinical validation. We identify four key challenges, specifically the insufficiency of automated metrics (e.g., BERTScore) to ensure medical safety and the limitations of standard \u201cDefinition of Done\u201d (DoD) in experimental contexts. As a solution, we implement \u201chybrid sprints\u201d incorporating \u201cquestion stories\u201d and multi-level acceptance criteria that prioritize clinical expert validation over technical scores. The results demonstrate that while standard agile methods provide a foundation, they require pragmatic decoupling of data and model lifecycles to ensure safety-critical value delivery.<\/jats:p>","DOI":"10.1007\/978-3-032-22375-3_15","type":"book-chapter","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:15:23Z","timestamp":1774930523000},"page":"244-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Agile Mobile Application Development with\u00a0Integrated Artificial Intelligence: The Case of\u00a0Diabetes Patient Support Application"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5440-3978","authenticated-orcid":false,"given":"Marks","family":"Calderon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6794-4726","authenticated-orcid":false,"given":"Carlos","family":"Celi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9138-8090","authenticated-orcid":false,"given":"Diego La","family":"Torre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Amershi, S., et al.: Software engineering for machine learning: a case study. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291\u2013300. IEEE (2019). https:\/\/doi.org\/10.1109\/ICSE-SEIP.2019.00042","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"15_CR2","unstructured":"Ambler, S.W.: The machine learning (ML) lifecycle. Ambysoft (2023). https:\/\/ambysoft.com\/essays\/machine-learning-lifecycle.html"},{"key":"15_CR3","unstructured":"Beck, K., et al.: Manifesto for agile software development. Agile Alliance (2001). https:\/\/agilemanifesto.org"},{"key":"15_CR4","unstructured":"Fielding, R.T.: Architectural Styles and the Design of Network-based Software Architectures. University of California, Irvine (2000)"},{"key":"15_CR5","unstructured":"Hohl, P., Gscheidle, C., Ludwigs, K.: Agile meets AI: A practical guide for applying agile frameworks to data science projects. Springer (2018)"},{"key":"15_CR6","unstructured":"International Diabetes Federation: IDF Diabetes Atlas, 11th edn. (2025). https:\/\/diabetesatlas.org\/"},{"issue":"4","key":"15_CR7","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1177\/0145721719853381","volume":"45","author":"LE Joensen","year":"2019","unstructured":"Joensen, L.E., Fisher, L., Skinner, T., Willaing, I.: Integrating the evidence on social support in diabetes self-management. Diabetes Educ. 45(4), 346\u2013358 (2019). https:\/\/doi.org\/10.1177\/0145721719853381","journal-title":"Diabetes Educ."},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Oates, B. J.: Researching Information Systems and Computing. 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Eng."},{"key":"15_CR11","unstructured":"Schwaber, K., Sutherland, J.: The Scrum Guide. Scrum.org (2020). https:\/\/scrumguides.org"},{"key":"15_CR12","unstructured":"Washizaki, H., Uchida, H., Khomh, F., Gu\u00e9h\u00e9neuc, Y.G.: Studying software engineering patterns for machine learning. In: 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 375\u2013385. 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