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Bio-Design and Manufacturing  2024 Vol.7 No.5 P.823

http://doi.org/10.1007/s42242-024-00313-2


Correction: Jetting-based bioprinting: process, dispense physics, and applications


Author(s):  Wei Long Ng, Viktor Shkolnikov

Affiliation(s):  Singapore Centre for 3D Printing (SC3DP), School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore; more

Corresponding email(s):   ng.wl@ntu.edu.sg, viktors@hp.com

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Wei Long Ng, Viktor Shkolnikov. Correction: Jetting-based bioprinting: process, dispense physics, and applications[J]. Journal of Zhejiang University Science D, 2024, 7(5): 823.

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Abstract: 
The article Jetting-based bioprinting: process, dispense physics, and applications, written by Wei Long Ng and Vik tor Shkolnikov, was originally published electronically on the publishers internet portal on 12 July 2024 without open access. With the author(s) decision to opt for Open Choice the copyright of the article changed on 24 July 2024 to The Author(s) 2024] and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribu tion and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the articles Cre ative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the articles Creative Commons licence and your intended use is not per mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copy right holder. To view a copy of this licence, visit http://creati vecommons.org/licenses/by/4.0.

牛津大学Jeroen H. M. Bergmann等 | 用大语言模型(LLM)对医疗器械软件分类评价

本文为牛津大学 Bergmann教授团队关于医疗器械分类的观点文章。医疗器械分类是根据器械的预定用途、相关风险等级和所需的监管监督来系统地分类器械,以确保器械符合监管机构规定的安全性和有效性标准。国际医疗器械监管论坛(IMDRF)指出,医疗产品必须符合全球各司法辖区制定的监管标准。美国将设备系统地分为三大类(I类、II类和III类);相比之下,欧盟采用更详细的分类框架,将设备分为四类(I类、IIa类、IIb类和III类)。分类错误可能导致重大运营和财务损失,如进行不必要的临床试验或市场准入申请被拒。早期识别正确的分类对于制造商来说很重要,以便量身定制证据的开展,和生成强有力的监管递交材料。由于不同国家的监管环境存在显著差异,分类标准也有所不同,这给企业的全球合规工作带来了挑战。
大语言模型(LLMs)基于从互联网、文献和各种文本材料中获取的大量数据进行训练,代表了人工智能领域的重要进展。这些模型在理解复杂语言模式、语境和语义方面表现出色。这种能力使大语言模型在理解、生成人类语言方面具有前所未有的能力,成为支持监管决策的潜在工具。我们的分析涵盖了包括中国、美国和欧洲在内的重要市场的医疗器械,评估了大语言模型在支持医疗器械软件分类方面的能力。我们实验了130种不同的医疗软件产品:包括66种中国国家药品监督管理局(NMPA)器械,29种美国食品药品监督管理局(FDA)器械,以及35种欧盟数据库器械。对于中国的分类(n=66),我们发现ERNIE Bot和Baichuan在单任务设备分类中的准确率分别为100%和97%。在美国器械上,GPT-3.5、Qwen和Mistral Large的准确率都达到了79%。但是,Baichuan和ERNIE Bot未能正确分类任何美国设备,这表明它们与美国设备的特征存在显著不匹配,或其训练数据中可能存在空白。值得注意的是,对几乎所有模型欧盟器械的测试结果都是错误的。这可能表明大语言模型在学习过程中缺乏可用的欧盟器械信息。这项研究标志着大语言模型在医疗监管环境中的初步探索,也提示了先进计算工具在简化和加强医疗监管流程中的潜力。

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