From screens to scenes: A survey of embodied AI in healthcare

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion Pub Date : 2025-02-21 DOI:10.1016/j.inffus.2025.103033

Yihao Liu , Xu Cao , Tingting Chen , Yankai Jiang , Junjie You , Minghua Wu , Xiaosong Wang , Mengling Feng , Yaochu Jin , Jintai Chen

{"title":"From screens to scenes: A survey of embodied AI in healthcare","authors":"Yihao Liu , Xu Cao , Tingting Chen , Yankai Jiang , Junjie You , Minghua Wu , Xiaosong Wang , Mengling Feng , Yaochu Jin , Jintai Chen","doi":"10.1016/j.inffus.2025.103033","DOIUrl":null,"url":null,"abstract":"<div><div>Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and personalization. Modern artificial intelligence (AI) has shown promise in addressing these issues through precise predictive modeling; however, its impact remains constrained by limited integration into clinical workflows. Powered by modern AI technologies such as multimodal large language models and world models, Embodied AI (EmAI) represents a transformative frontier, offering enhanced autonomy and the ability to interact with the physical world to address these challenges. As an interdisciplinary and rapidly evolving research domain, “EmAI in healthcare” spans diverse fields such as algorithms, robotics, and biomedicine. This complexity underscores the importance of timely reviews and analyses to track advancements, address challenges, and foster cross-disciplinary collaboration. In this paper, we provide a comprehensive overview of the “brain” of EmAI for healthcare, wherein we introduce foundational AI algorithms for perception, actuation, planning, and memory, and focus on presenting the healthcare applications spanning clinical interventions, daily care & companionship, infrastructure support, and biomedical research, covering 35 specialized tasks. These significant advancements have the potential to enable personalized care, enhance diagnostic accuracy, and optimize treatment outcomes. Despite its promise, the development of EmAI for healthcare is hindered by critical challenges such as safety concerns, gaps between simulation platforms and real-world applications, the absence of standardized benchmarks, and uneven progress across interdisciplinary domains. We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare. A hierarchical framework of intelligent levels for EmAI systems is also introduced to guide further development. By providing systematic insights, this work aims to inspire innovation and practical applications, paving the way for a new era of intelligent, patient-centered healthcare.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"119 ","pages":"Article 103033"},"PeriodicalIF":14.7000,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Fusion","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S156625352500106X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and personalization. Modern artificial intelligence (AI) has shown promise in addressing these issues through precise predictive modeling; however, its impact remains constrained by limited integration into clinical workflows. Powered by modern AI technologies such as multimodal large language models and world models, Embodied AI (EmAI) represents a transformative frontier, offering enhanced autonomy and the ability to interact with the physical world to address these challenges. As an interdisciplinary and rapidly evolving research domain, “EmAI in healthcare” spans diverse fields such as algorithms, robotics, and biomedicine. This complexity underscores the importance of timely reviews and analyses to track advancements, address challenges, and foster cross-disciplinary collaboration. In this paper, we provide a comprehensive overview of the “brain” of EmAI for healthcare, wherein we introduce foundational AI algorithms for perception, actuation, planning, and memory, and focus on presenting the healthcare applications spanning clinical interventions, daily care & companionship, infrastructure support, and biomedical research, covering 35 specialized tasks. These significant advancements have the potential to enable personalized care, enhance diagnostic accuracy, and optimize treatment outcomes. Despite its promise, the development of EmAI for healthcare is hindered by critical challenges such as safety concerns, gaps between simulation platforms and real-world applications, the absence of standardized benchmarks, and uneven progress across interdisciplinary domains. We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare. A hierarchical framework of intelligent levels for EmAI systems is also introduced to guide further development. By providing systematic insights, this work aims to inspire innovation and practical applications, paving the way for a new era of intelligent, patient-centered healthcare.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Information Fusion 工程技术-计算机：理论方法

CiteScore

33.20

自引率

4.30%

发文量

161

审稿时长

7.9 months

期刊介绍： Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.