过程(计算)
计算机科学
3D生物打印
人工智能
劳动力
管理科学
数据科学
系统工程
风险分析(工程)
工程类
业务
政治学
法学
组织工程
生物医学工程
操作系统
作者
Srikanthan Ramesh,Akash Deep,Ali Tamayol,Abishek B. Kamaraj,Chaitanya Mahajan,Sundararajan V. Madihally
出处
期刊:Bioprinting
[Elsevier]
日期:2024-04-01
卷期号:38: e00331-e00331
被引量:1
标识
DOI:10.1016/j.bprint.2024.e00331
摘要
3D bioprinting, a vital tool in tissue engineering, drug testing, and disease modeling, is increasingly integrated with machine learning (ML) and artificial intelligence (AI). Although some existing reviews acknowledge this integration, a detailed examination of system and process challenges remains to be discussed. This review divides the topic into two main areas: the process view, which sees bioprinting as a standalone system and outlines data-driven solutions for challenges such as material selection, parameter optimization, and real-time monitoring, and the system view, which delves into the broader ecosystem of bioprinting and its interaction with other technologies. We first present the latest techniques in managing process-specific challenges using ML/AI, highlighting future opportunities. We then navigate through system-wide challenges, emphasizing data-driven solutions. This review also sheds light on potential regulatory frameworks and the need for skilled workforce development, advocating for an alignment between policy and technology progression.
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