Improving Regulatory Compliance in AI Software as a Medical Device: A Mixed-Methods Analysis of Stakeholder Perspectives and Design Strategies (Preprint)
Authors: Wang Kuan-Chung , Tsai I-Ching , Wang Shun-min , Chen Peng-Ting 
Published: November 2024

BACKGROUND

AI integration into medical devices is an inevitable trend, poised to redefine the healthcare landscape. These AI-driven devices can process vast amounts of patient data with unparalleled speed, enabling more accurate diagnostics and tailored treatment approaches. With AI at the core, medical devices are becoming smarter and more responsive, providing real-time insights that support clinicians in making precise, data-backed decisions. As this technology advances, it will drive greater efficiency, reduce medical errors, and enhance the quality of patient care, marking a new era where AI is essential in modern medicine. However, the commercialization of AI SaMD may face significant challenges due to insufficient regulatory frameworks, inadequate design processes, and poor collaboration among stakeholders. Failure to address these development challenges may lead to wasted resources and ultimately unsuccessful outcomes.

OBJECTIVE

This study aims to guide medical device manufacturers in effectively adapting to this trend and understanding strategies for complying with medical regulations in the AI device design process. Manufacturers must stay updated on regulatory changes, particularly in specialized training and regulatory review processes. This will help balance innovation with compliance, enabling a smooth advancement of AI-driven technology in medical devices.

METHODS

This study constructs an analytical framework based on a comprehensive literature review and interviews with key stakeholders involved in AI SaMD development, including academics, researchers, manufacturers, clinical participants, and regulatory consultants. Following the framework's establishment, the Imperative-External Assistance Analysis (IEA) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods were utilized to identify the interaction structures among the identified factors. This approach facilitates the determination of effective coping strategies that enhance the efficacy of AI SaMD development and implementation.

RESULTS

This study collected data from interviews with 16 stakeholders and analyzed 68 questionnaire responses. It proposes an AI design process evaluation framework comprising 17 factors across four stages: Pre-processing, Model Establishment, Clinical Evaluation, and Registration. This framework was applied to assess AI SaMD design and development and identify improvement strategies. Specifically, it emphasizes strengthening internal management during the Pre-processing stage, retaining the current practices in Model Establishment, and seeking external expertise for the Registration and Clinical Evaluation stages. Tailored recommendations for each development stage are also provided to support effective AI SaMD development.

CONCLUSIONS

This study examines the design and development of AI-based Software as a Medical Device (SaMD) and introduces a feasibility analysis model that integrates regulatory requirements. It underscores the critical role of collaboration among software engineers, medical professionals, and regulatory experts for successful AI SaMD development. Additionally, this study serves as a practical guide for stakeholders in the AI SaMD industry, fostering collaboration and building consensus. By adopting effective strategies early in the design and development process, stakeholders can accelerate AI SaMD innovation, achieve regulatory compliance more efficiently, and ultimately enhance healthcare quality.

CLINICALTRIAL

not applicable