Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications

IF 3.3 2区医学 Q2 ENGINEERING, BIOMEDICAL

Journal of the Mechanical Behavior of Biomedical Materials Pub Date : 2025-02-25 DOI:10.1016/j.jmbbm.2025.106949

Saman Mohammadnabi , Nima Moslemy , Hadi Taghvaei , Abdul Wasy Zia , Sina Askarinejad , Faezeh Shalchy

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Abstract

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

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人工智能在以数据为中心的生物医学应用增材制造过程中的作用

增材制造（AM）在医疗保健应用中的作用越来越大，特别是在满足特定主题要求的愿望方面。本文回顾了人工智能（AI）在增强am前、am中和am后过程中的应用，以满足更广泛的医疗保健干预的特定主题要求。本文介绍了常见的AM流程和AI工具，如监督学习、无监督学习、深度学习和强化学习。从结构设计与图像重建、材料设计与配方、加工参数等核心维度描述了人工智能在预处理中的作用。AI在打印过程中的作用是基于硬件规格、打印配置和核心操作参数（如温度）来描述的。同样，后处理描述了人工智能在表面处理、尺寸精度、固化过程中的作用，以及增材制造过程与生物活性之间的关系。后面的部分提供了详细的科学计量学研究，主题主题的专题评估，并反映了生物医学应用AM中的人工智能伦理。这篇综述文章认为人工智能是生物医学产品增材制造的一个强大而强大的工具。从组织工程（TE）到假肢，从芯片上的实验室到芯片上的器官，以及一系列产品的添加剂生物制造；人工智能在筛选所需的过程-属性-性能关系方面具有很高的潜力，可用于资源高效的am前后周期，以开发具有增强的特定主题合规性规范的高质量医疗保健产品。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Mechanical Behavior of Biomedical Materials 工程技术-材料科学：生物材料

CiteScore

7.20

自引率

7.70%

发文量

505

审稿时长

46 days

期刊介绍： The Journal of the Mechanical Behavior of Biomedical Materials is concerned with the mechanical deformation, damage and failure under applied forces, of biological material (at the tissue, cellular and molecular levels) and of biomaterials, i.e. those materials which are designed to mimic or replace biological materials. The primary focus of the journal is the synthesis of materials science, biology, and medical and dental science. Reports of fundamental scientific investigations are welcome, as are articles concerned with the practical application of materials in medical devices. Both experimental and theoretical work is of interest; theoretical papers will normally include comparison of predictions with experimental data, though we recognize that this may not always be appropriate. The journal also publishes technical notes concerned with emerging experimental or theoretical techniques, letters to the editor and, by invitation, review articles and papers describing existing techniques for the benefit of an interdisciplinary readership.