Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last decade.To assess historical research trends and current advances,we conducted an ar...Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last decade.To assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ophthalmology.Methods:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of Science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting information.After verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 articles.Results:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL domain.The publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most frequent.Among the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical insignificance.Furthermore,43 publicly available ocular image datasets were summarized.Conclusion:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing areas.This study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.展开更多
[目的/意义]在具身智能从工业自动化转向民生服务的战略背景下,社交机器人面临交互粘性不足与情境理解匮乏的现实困境,情感计算作为赋予机器感知、理解与模拟人类情感的核心技术,是支撑具身智能实现社会化的关键。研究旨在解析多模态感...[目的/意义]在具身智能从工业自动化转向民生服务的战略背景下,社交机器人面临交互粘性不足与情境理解匮乏的现实困境,情感计算作为赋予机器感知、理解与模拟人类情感的核心技术,是支撑具身智能实现社会化的关键。研究旨在解析多模态感知、动态适应策略与伦理边界的技术路径,为构建负责人智交互体系提供理论参考。[方法/过程]遵循PRISMA导向,检索Web of Science近10年具身智能与情感计算交叉领域文献。基于具身性、技术完整性及交互实证性标准筛选,因内容完整性剔除无法获取全文条目,最终选取97篇核心文献。从视觉鲁棒感知、副语言解码、生理信号洞察及多源异构数据融合等维度解析感知层级,并探讨大语言模型驱动下的生成式适应策略。[结果/结论]社交机器人情感计算正经历从单一信号统计向多模态语义融合、从静态规则映射向生成式动态适应的范式演进。研究证实,多模态感知的实质是对人类意图的深度解构而非简单的数据统计,基于此,本研究构建了以情境理解为起点、适应行动为核心、伦理约束为底线的动态交互框架。该框架强调,情感适应应从机械模仿转向认知共情,通过大语言模型驱动的生成式策略实现交互的个性化与连贯性,同时伦理边界并非外部附加的规制,而应是内生于算法决策的逻辑约束,旨在应对隐私不对称与心理操纵等内生风险。未来的创新范式应立足于真实环境的生态效度,通过融合长期记忆的终身学习机制对抗新奇效应的消退,并建立人在回路的安全熔断机制,从而确保具身智能在介入人类精神世界过程中的主权安全与科技向善。展开更多
基金supported by the National Natural Science Foundation of China(82000946)Guangdong Natural Science Funds for Distinguished Young Scholar(2023B1515020100)+1 种基金the Natural Science Foundation of Guangdong Province(2021A1515012238)the Science and Technology Program of Guangzhou(202201020522 and 202201020337).
文摘Background:Research innovations inocular disease screening,diagnosis,and management have been boosted by deep learning(DL)in the last decade.To assess historical research trends and current advances,we conducted an artificial intelligence(AI)-human hybrid analysis of publications on DL in ophthalmology.Methods:All DL-related articles in ophthalmology,which were published between 2012 and 2022 from Web of Science,were included.500 high-impact articles annotated with key research information were used to fine-tune a large language models(LLM)for reviewing medical literature and extracting information.After verifying the LLM's accuracy in extracting diseases and imaging modalities,we analyzed trend of DL in ophthalmology with 2535 articles.Results:Researchers using LLM for literature analysis were 70%(P=0.0001)faster than those who did not,while achieving comparable accuracy(97%versus 98%,P=0.7681).The field of DL in ophthalmology has grown 116%annually,paralleling trends of the broader DL domain.The publications focused mainly on diabetic retinopathy(P=0.0003),glaucoma(P=0.0011),and age-related macular diseases(P=0.0001)using retinal fundus photographs(FP,P=0.0015)and optical coherence tomography(OCT,P=0.0001).DL studies utilizing multimodal images have been growing,with FP and OCT combined being the most frequent.Among the 500 high-impact articles,laboratory studies constituted the majority at 65.3%.Notably,a discernible decline in model accuracy was observed when categorizing by study design,notwithstanding its statistical insignificance.Furthermore,43 publicly available ocular image datasets were summarized.Conclusion:This study has characterized the landscape of publications on DL in ophthalmology,by identifying the trends and breakthroughs among research topics and the fast-growing areas.This study provides an efficient framework for combined AI-human analysis to comprehensively assess the current status and future trends in the field.
文摘[目的/意义]在具身智能从工业自动化转向民生服务的战略背景下,社交机器人面临交互粘性不足与情境理解匮乏的现实困境,情感计算作为赋予机器感知、理解与模拟人类情感的核心技术,是支撑具身智能实现社会化的关键。研究旨在解析多模态感知、动态适应策略与伦理边界的技术路径,为构建负责人智交互体系提供理论参考。[方法/过程]遵循PRISMA导向,检索Web of Science近10年具身智能与情感计算交叉领域文献。基于具身性、技术完整性及交互实证性标准筛选,因内容完整性剔除无法获取全文条目,最终选取97篇核心文献。从视觉鲁棒感知、副语言解码、生理信号洞察及多源异构数据融合等维度解析感知层级,并探讨大语言模型驱动下的生成式适应策略。[结果/结论]社交机器人情感计算正经历从单一信号统计向多模态语义融合、从静态规则映射向生成式动态适应的范式演进。研究证实,多模态感知的实质是对人类意图的深度解构而非简单的数据统计,基于此,本研究构建了以情境理解为起点、适应行动为核心、伦理约束为底线的动态交互框架。该框架强调,情感适应应从机械模仿转向认知共情,通过大语言模型驱动的生成式策略实现交互的个性化与连贯性,同时伦理边界并非外部附加的规制,而应是内生于算法决策的逻辑约束,旨在应对隐私不对称与心理操纵等内生风险。未来的创新范式应立足于真实环境的生态效度,通过融合长期记忆的终身学习机制对抗新奇效应的消退,并建立人在回路的安全熔断机制,从而确保具身智能在介入人类精神世界过程中的主权安全与科技向善。
