This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative ...This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative potentialities, issues and ethical concerns. The review consolidates findings that show the impact of AI in improving the efficiency, accuracy and justice delivery in the legal profession. The studies show increased efficiency in speed of document review and enhancement of the accuracy of the reviewed documents, with time efficiency estimates of 60% reduction of time. However, the review also outlines some of the problems that continue to characterize AI, such as data quality problems, biased algorithms and the problem of the opaque decision-making system. This paper assesses ethical issues related to patient autonomy, justice and non-malignant suffering, with particular focus on patient privacy and fair process, and on potential unfairness to patients. This paper’s review of AI innovations finds that regulations lag behind AI developments, leading to unsettled issues regarding legal responsibility for AI and user control over AI-generated results and findings in legal proceedings. Some of the future avenues that are presented in the study are the future of XAI for legal purposes, utilizing federated learning for resolving privacy issues, and the need to foster adaptive regulation. Finally, the review advocates for Legal Subject Matter Experts to collaborate with legal informatics experts, ethicists, and policy makers to develop the best solutions to implement AI in medical negligence claims. It reasons that there is great potential for AI to have a deep impact on the practice of law but when done, it must do so in a way that respects justice and on the Rights of Individuals.展开更多
The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same ...The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian <i>M<sup>L×K</sup>/M<sup>L×K</sup>/</i>1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. <span style="white-space:normal;"><i></i></span><i>In fine<span style="white-space:normal;"></span></i>, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.展开更多
随着深度学习技术的发展,光学字符识别(optical character recognition,OCR)技术逐步从传统方法转向基于深度神经网络的端到端学习模型,涌现出大量具备高准确率和强泛化能力的OCR大模型。多模态大模型通过融合视觉、语言等多种感知通道...随着深度学习技术的发展,光学字符识别(optical character recognition,OCR)技术逐步从传统方法转向基于深度神经网络的端到端学习模型,涌现出大量具备高准确率和强泛化能力的OCR大模型。多模态大模型通过融合视觉、语言等多种感知通道,提高了模型在复杂场景下的理解与生成能力,而多任务统一大模型则通过构建通用架构,简化了模型设计,提升了多个OCR任务的处理效率。本文回顾了OCR和多模态学习领域的最新技术进展,重点介绍了OCR大模型在多模态学习和多任务统一模型中的应用与前沿进展。此外,本文还分析了OCR增强的多模态大模型、文档理解多模态大模型和针对特定OCR任务的多模态大模型的现状与挑战,探讨了OCR大模型面临的技术瓶颈和未来发展方向,包括提升分辨率处理能力、改进视觉标记压缩、增强结构化图形符号和复杂版面结构的感知与理解等,展望了其在文档数字化、程序自动化测试和智能教育等方面的广泛应用潜力。展开更多
文摘This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative potentialities, issues and ethical concerns. The review consolidates findings that show the impact of AI in improving the efficiency, accuracy and justice delivery in the legal profession. The studies show increased efficiency in speed of document review and enhancement of the accuracy of the reviewed documents, with time efficiency estimates of 60% reduction of time. However, the review also outlines some of the problems that continue to characterize AI, such as data quality problems, biased algorithms and the problem of the opaque decision-making system. This paper assesses ethical issues related to patient autonomy, justice and non-malignant suffering, with particular focus on patient privacy and fair process, and on potential unfairness to patients. This paper’s review of AI innovations finds that regulations lag behind AI developments, leading to unsettled issues regarding legal responsibility for AI and user control over AI-generated results and findings in legal proceedings. Some of the future avenues that are presented in the study are the future of XAI for legal purposes, utilizing federated learning for resolving privacy issues, and the need to foster adaptive regulation. Finally, the review advocates for Legal Subject Matter Experts to collaborate with legal informatics experts, ethicists, and policy makers to develop the best solutions to implement AI in medical negligence claims. It reasons that there is great potential for AI to have a deep impact on the practice of law but when done, it must do so in a way that respects justice and on the Rights of Individuals.
文摘The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian <i>M<sup>L×K</sup>/M<sup>L×K</sup>/</i>1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. <span style="white-space:normal;"><i></i></span><i>In fine<span style="white-space:normal;"></span></i>, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.
文摘随着深度学习技术的发展,光学字符识别(optical character recognition,OCR)技术逐步从传统方法转向基于深度神经网络的端到端学习模型,涌现出大量具备高准确率和强泛化能力的OCR大模型。多模态大模型通过融合视觉、语言等多种感知通道,提高了模型在复杂场景下的理解与生成能力,而多任务统一大模型则通过构建通用架构,简化了模型设计,提升了多个OCR任务的处理效率。本文回顾了OCR和多模态学习领域的最新技术进展,重点介绍了OCR大模型在多模态学习和多任务统一模型中的应用与前沿进展。此外,本文还分析了OCR增强的多模态大模型、文档理解多模态大模型和针对特定OCR任务的多模态大模型的现状与挑战,探讨了OCR大模型面临的技术瓶颈和未来发展方向,包括提升分辨率处理能力、改进视觉标记压缩、增强结构化图形符号和复杂版面结构的感知与理解等,展望了其在文档数字化、程序自动化测试和智能教育等方面的广泛应用潜力。
