2026-05-04T23:18:20.798Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:11659 2025-10-27T16:07:04Z 1

Software Quality and Compliance in Intelligent Health Monitoring Systems: A Case Study of Baby FM 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/11659 https://ceur-ws.org/Vol-4077/paper7.pdf B. Gutić T. Papić P. Dakić Integrating artificial intelligence (AI) into wearable health monitoring systems introduces both innovation and regulatory complexity, requiring a standardized approach to ensure patient safety, software robustness, and compliance with international medical device regulations. This paper presents a comprehensive case study of Baby FM, an AI-powered wearable medical device developed for continuous temperature monitoring in pediatric and veterinary care. The system combines real-time sensing, secure cloud connectivity, and anomaly detection through interpretable AI models. We detail a structured approach to achieving high software quality through the implementation of a customized Quality Management System (QMS), in compliance with ISO 13485, ISO 14971, and IEC 62304. The QMS supported modular documentation, traceability, risk control, and test automation throughout the product life cycle. In addition, the study outlines the preparation of technical documentation for CE and ALIMS certification, including verification and validation evidence, clinical benefit justification, and post-market surveillance planning. Beyond the internal development and compliance strategy, this paper provides a comparative overview of standard adoption in multiple industries, including the med-tech, pharmaceutical, and industrial IoT sectors. This case study presents several key findings: Modular QMS implementation enabled incremental regulatory compliance without stalling agile software development, Early adoption of ISO 13485, ISO 14971, IEC 62304, and MDR 2017/745 reduced regulatory friction and aligned documentation with development milestones, Embedding explainable AI techniques (SHAP visualization and audit trails) improved transparency, clinician trust, and regulator acceptance. Comparative analysis confirms that medtech devices require more stringent certification than pharma and health IT but deliver stronger post-market accountability. The scalable architecture supports future extensions in oncology, fertility, and livestock monitoring. The findings illustrate how practices from these sectors can inform the development of intelligent health systems and guide strategic decisions for startups seeking certification. Insights from real-world clinical trials, regulatory interactions, and AI transparency strategies offer a replicable methodology for innovators looking to balance agility and compliance in the development of AI-driven medical products. conferenceObject https://ceur-ws.org/Vol-4077/paper7.pdf https://ceur-ws.org/Vol-4077/paper7.pdf