{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:59:53Z","timestamp":1790369993785,"version":"4.1.0"},"reference-count":15,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"<jats:p>Digital therapeutics (DTx) are emerging as evidence-based software interventions, but current AI-driven personalization approaches lack dedicated safety-focused frameworks and face challenges due to scarce long-term outcome data and unpredictable model behaviors. We propose SAFE_DTx, a safety-first architectural framework for DTx that integrates well-established principles of predictive modeling and constrained decision-making to prioritize patient safety. SAFE_DTx\u2019s 2-module architecture comprises an AI feedback prediction module that forecasts short-term patient responses and a constrained planning module that selects the next intervention under explicit safety constraints. By decoupling these components and enforcing clear safety guardrails, the framework enables dynamic, real-time adaptation to individual patient feedback while staying within evidence-based safety limits. This modular design also enhances transparency in the decision-making process, and an in silico evaluation demonstrates its preliminary architectural feasibility, showing greater engagement and no safety violations compared to baseline strategies within the simulated environment. SAFE_DTx\u2019s safety-by-design architecture aligns with emerging regulatory emphasis on AI transparency and patient safety. It directly addresses key clinical challenges in AI-driven DTx personalization by ensuring that tailored interventions do not compromise patient safety.<\/jats:p>","DOI":"10.2196\/78202","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:05:06Z","timestamp":1790366706000},"page":"e78202","source":"Crossref","is-referenced-by-count":0,"title":["SAFE_DTx: Safety-First Framework for AI-Driven Personalization in Digital Therapeutics"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2022-6333","authenticated-orcid":false,"given":"Dohyoung","family":"Rim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00777-z"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjqs-2018-008370"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01316-0"},{"key":"ref4","first-page":"230","author":"Weimann, TG","year":"2024","journal-title":"Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.5555\/2789272.2886795"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s44230-025-00093-7"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.2196\/52688"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30017-0"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-024-03045-0"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/da.22822"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1109\/SWC62898.2024.00112","author":"Islam, R","year":"2024","journal-title":"2024 IEEE Smart World Congress (SWC)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/jpm13101523"},{"key":"ref13","unstructured":"Artificial intelligence\/machine learning (AI\/ML)-based Software as a Medical Device (SaMD) action planU.S. Food & Drug Administration2021012026-09-09https:\/\/www.fda.gov\/media\/145022\/download"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00386-z"},{"key":"ref15","first-page":"3504","author":"Choi, E","year":"2016","journal-title":"NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e78202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:05:08Z","timestamp":1790366708000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e78202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":15,"URL":"https:\/\/doi.org\/10.2196\/78202","relation":{"has-preprint":[{"id-type":"doi","id":"10.2196\/preprints.78202","asserted-by":"object"}]},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]}}}