Machine Learning and Medical Devices:The Next Step for Tissue Engineering

• 作者: Hannah A.Pearce Antonios G.Mikos
• 来源期刊: 工程（英文）
• 年，卷（期）: 2021,（12）
• 所属期刊栏目: Biomedical Engineering
• 页码范围: 1704-1706
• 页数: 3页
• 语种: 英文

摘要

The pathway for creating and seeing a medical device to market is time-intensive,costly,and demanding[1,2].This is particularly true of devices developed with tissue engineering components within the device[1,3].Machine learning and artificial intelligence have expedited optimization and engineering design in many other engineering disciplines[4,5].By developing and using machine learning algorithms,novel drugs and enzymes have been discov-ered,innovative synthetic pathways and new chemistries have been identified,parameters for the three-dimensional printing of tissue engineering scaffolds have been elucidated,and better pre-dictors for the success of electronics and robotics have been demonstrated[6-9].Although machine learning and artificial intelligence have begun to be integrated into engineering and science research,medical device research and tissue engineering have not kept pace with this trend[1,4].A wealth of underutilized data is available in the form of research studies,clinical studies,and medical device and patent applications[1,4].There are also enormous costs and complex regulatory pathways in seeing medical devices-particularly tissue engineering devices-to market[1,2,10].Utilizing machine learning will enable the design of innovative,cost-efficient,and effective tissue engineering medical devices while minimizing the time to market.Harnessing the power of machine learning is the next step in the evolution of medical device development and is key for the continued success of tissue engineering.