<p dir="ltr">The use of connected health technology is becoming increasingly significant in the field of healthcare. Artificial Intelligence- augmented workflows connected to treatment guidelines promise more inclusive care delivery. The AI/ML-based connected health informatics plays an integral role in every stage of medical product development, from initial discovery to providing guidance to healthcare providers, and finally to delivering patient care. The exponential growth of meta data and the rapid advancement of connected health technologies provide greater opportunities for novel healthcare solutions, delivery mechanisms, and clinical trial designs.</p><p dir="ltr">However, it poses complexity of the AI/ML-specific challenges besides all the challenges SaMD products face. The regulations for AI/ML-based connected solutions have yet to mature. The AI/ML SaMD development process requires additional considerations such as data quality and management, continuous deployment, and validation.</p><p><br></p><p dir="ltr">This study delves into the integration of Machine Learning (ML) with medical software devices and how the current lifecycle models fit the needs of the AI industry. AI/ML-based SaMD development process artifacts are identified through the theory and AI/ML SaMD regulations and standards requirements. Moreover, this study analyzes collected data from interviews, surveys, and an experimental case study to identify success factors in building quality and agility for AI/ML-based SaMD development projects.</p><p dir="ltr">Incorporating of Artificial Intelligence (AI) in healthcare requires continuous deployment and validation processes, which may not be in line with the current workflow, capability, or authority of regulators. This research also highlights that model governance and technology access can be key challenges in implementing AI/ML development process artifacts, especially when integrated into connected health solutions.</p><p dir="ltr">This work sets the foundation for future research to reduce bottlenecks in the machine-learning process. The focus should be on aiding model governance to streamline development and ensure machine reliability. A suitable software toolchain is necessary for exploratory data analysis, data integration, documentation, model governance, monitoring, version control, and integration with other software and services within a connected health solution. Additionally, conducting more focused research on security and privacy in the context of connected health would be valuable.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25670067 |
| Date | 24 April 2024 |
| Creators | Niusha Nikfal (18424854) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_sup_strong_A_DEVELOPMENT_PROCESS_FRAMEWORK_FOR_strong_sup_sup_strong_ARTIFICIAL_INTELLIGENCE_MACHINE_LEARNING_AI_ML_-BASED_strong_sup_sup_strong_CONNECTED_HEALTH_INFORMATICS_strong_sup_/25670067 |
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