Generative AI for Healthcare Engineering and Technology Challenges

Abstract

Healthcare field engineers play a critical role in ensuring the smooth operation and maintenance of medical equipment. However, they face numerous challenges such as adhering to standard operating procedures (SOPs), managing inventory, maintaining equipment quality, and optimizing time allocation. This research paper proposes a novel approach that harnesses the power of generative artificial intelligence (AI) to overcome these challenges. In this study, generative AI algorithms are employed to develop an intelligent system that assists healthcare field engineers in following SOPs accurately while being always compliant. This is aimed to ensure consistent and efficient procedures, leading to improved equipment performance and patient safety. Additionally, the system integrates generative AI techniques to uphold equipment quality. It transforms lengthy equipment manuals into interactive Q&A systems, enabling engineers to focus on their tasks and access key information as needed. This enhances engineer productivity and indirectly contributes to the equipment’s working quality. While from a use case perspective, generative AI seems to effectively solve the problem of manually referring SOPs, compliance manuals and product catalogs. There would be technology challenges (especially around Artificial Intelligence) like data security, geo-political influences on data governance, dependency on specific technology platforms in addition to maintaining such systems over time effectively. In summary, this research introduces an innovative solution to address challenges faced by healthcare field engineers through the application of generative AI. By utilizing machine learning algorithms, the proposed system enhances adherence to standard operating procedures (SOPs), streamlines inventory management, improves equipment quality maintenance, and optimizes time management. The study’s outcomes contribute to the efficient implementation of SOP adherence and process guidelines, while also providing guidelines to tackle long-term challenges related to technology maintenance, ethical compliance of AI systems, mitigation of risks and data governance influenced by the dynamic geopolitical landscape.