Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. However, the R&D process of AI medical software as a medical device (AI SaMD) has to follow the product lifecycle-based regulatory framework design by the regulatory authority to ensure that the safety and effectiveness of the software as a medical device is maintained. This study aims to construct the R&D and commercialization analysis model and clarifies the resource allocation strategy of artificial intelligence in software as a medical device. The R&D and commercialization categories are first collected from the literature review and the current industrial practical cases. The content analysis is used to analyze the interviews with the key stakeholders engaging in AI SaMD development and commercialization. The Imperative-External Assistance analysis model and Decision-Making Trial and Evaluation Laboratory (DEMATEL) are both applied next to quantitatively identify the interaction structure between the factors. The Imperative-External Assistance analysis model is designed and used to conduct IEA-NRM to find out the coping strategy. Then, the interaction structure is used to construct the analytic network process (ANP) evaluation framework. Through applying ANP, the relative importance and resource allocation strategy are identified. Moreover, the responsive strategies of overcoming innovative resistances and improving application development for hospitals are also discussed. This study expects to propose the R&D and commercialization analysis model and resource allocation strategy of AI SaMD for increasing the stakeholders’ corporation efficiency and effectiveness.
|Effective start/end date||20-08-01 → 21-07-31|
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