Grand Challenge: Applying Regulatory Science and Big Data to Improve Medical Device Innovation
| Autor: | Arthur G. Erdman, Daniel F. Keefe, R. Schiestl |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Device Approval
Computer science business.industry Interface (computing) Big data Biomedical Engineering Computational Biology Equipment Design computer.software_genre Models Biological Visualization Work (electrical) Risk analysis (engineering) Animals Computer-Aided Design Humans Computer Aided Design Computer Simulation Regulatory science Product (category theory) business computer Simulation |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 60:700-706 |
| ISSN: | 1558-2531 0018-9294 |
| DOI: | 10.1109/tbme.2013.2244600 |
| Popis: | Understanding how proposed medical devices will interface with humans is a major challenge that impacts both the design of innovative new devices and approval and regulation of existing devices. Today, designing and manufacturing medical devices requires extensive and expensive product cycles. Bench tests and other preliminary analyses are used to understand the range of anatomical conditions, and animal and clinical trials are used to understand the impact of design decisions upon actual device success. Unfortunately, some scenarios are impossible to replicate on the bench, and competitive pressures often accelerate initiation of animal trials without sufficient understanding of parameter selections. We believe that these limitations can be overcome through advancements in data-driven and simulation-based medical device design and manufacturing, a research topic that draws upon and combines emerging work in the areas of Regulatory Science and Big Data. We propose a cross-disciplinary grand challenge to develop and holistically apply new thinking and techniques in these areas to medical devices in order to improve and accelerate medical device innovation. |
| Databáze: | OpenAIRE |
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