软件部署
工作流程
数据科学
多模态
范围(计算机科学)
计算机科学
钥匙(锁)
人工智能
可用的
利益相关者
知识管理
光学(聚焦)
深度学习
人机交互
持续性
术语
敏捷软件开发
多模式学习
分类
管理科学
语义学(计算机科学)
多样性(控制论)
作者
Xianyuan Liu,Jiayang Zhang,Shuo Zhou,Thijs L. van der Plas,Avish Vijayaraghavan,Anastasiia Grishina,Mengdie Zhuang,Daniel Schofield,Christopher Tomlinson,Yuhan Wang,Ruizhe Li,Louisa van Zeeland,Sina Tabakhi,Cyndie Demeocq,Xiang Li,Arunav Das,Orlando Timmerman,Thomas Baldwin-McDonald,Jinge Wu,Peizhen Bai
标识
DOI:10.1038/s42256-025-01116-5
摘要
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact.
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