artificial intelligence

Progress in mantle anisotropy research based on XKS phases

XKS shear-wave splitting has been widely applied to investigations of anisotropy in both the upper mantle and the lowermost mantle D" layer, and represents a key tool for probing mantle deformation and dynamics. The first part of this paper reviews the basic principles and major approaches of XKS splitting analysis, and summarizes its applications to upper-mantle anisotropy beneath the Chinese mainland, where strong regional variations in deformation are observed, primarily controlled by the subduction of the Indian plate and the western Pacific plate. We further discuss methods, applications, and challenges in using XKS phases to probe D" anisotropy, highlighting that complex and strong anisotropic structures are commonly observed beneath remnant slab regions and large low-shear-velocity provinces, while effective separation of upper-mantle contributions and mitigation of wavefield scattering and interference remain major difficulties. Over the past decade, with the rapid growth of seismic datasets and advances in inversion techniques, XKS-based three-dimensional anisotropic imaging of the upper mantle has become increasingly mature, significantly improving depth resolution of anisotropic structures. The second part of this paper introduces the theoretical framework and development of this imaging approach and summarizes recent results. This method is capable of delineating three-dimensional upper-mantle anisotropy and effectively identifying deep structures such as slab geometries, mantle upwellings, and layered anisotropy, although the imaging quality strongly depends on data coverage and observation density. Finally, we discuss future perspectives of XKS studies in mantle anisotropy research. The field is evolving from traditional parameter-based analyses toward full three-dimensional imaging. However, three-dimensional anisotropic imaging of both the upper mantle and the D" layer beneath the Chinese mainland remains limited. Future efforts should integrate multiple anisotropic constraints, promote multi-layer mantle coupling studies, and incorporate artificial intelligence techniques to improve the accuracy of shear-wave splitting analysis, thereby providing a foundation for constructing high-resolution, high-precision three-dimensional anisotropic models of the mantle beneath China.

Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.

Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and patient care. Recent advances in machine learning and deep learning have produced tools that help diagnose cancer, assess risk, and predict outcomes. In screening, AI-based risk calculators improve detection and help avoid unnecessary biopsies. Deep learning algorithms, particularly convolutional neural networks, have demonstrated expert-level performance in pathology, identifying malignancy and assigning Gleason grades with high accuracy. These tools also streamline workflow, flagging challenging cases for review and quantifying prognostic markers, such as Ki-67 and cribriform patterns. In addition, AI-based models can predict molecular alterations, microsatellite instability, and lymph node metastasis directly from histology images, providing cost-effective alternatives to traditional assays. The development of multimodal models integrates digital pathology and clinical parameters, enabling personalized treatment recommendations and improved outcome prediction. Natural language processing and large language models further expand AI's potential, facilitating information extraction from clinical notes and enhancing patient education. Despite these advances, most studies remain retrospective with heterogeneous endpoints. Performance often drops when models are tested at new sites because of differences in patient populations and slide preparation. Access to large, well-annotated datasets is limited, and technical variation hampers reproducibility. To move toward clinical use, the field needs prospective, multicenter validation, preanalytical and analytical standardization, and clear reporting of failure modes and human oversight. Emerging approaches, including self-supervised pretraining, transformer-based image models, and language-vision systems, are likely to improve generalization and support more personalized care.

Integration of functional magnetic resonance imaging and artificial intelligence in Alzheimer’s disease

Linguistic Justice and Artificial Intelligence

AI Ethics in Industry: A Research Framework

This paper discusses a research framework for implementing AI ethics in industrial settings and presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study

An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.

Implementing Ethics in AI: An industrial multiple case study

This paper provides a baseline for ethics in AI based software development by reporting results from an industrial multiple case study on AI systems development in the health care sector, and explores the current state of practice out on the field in the absence of formal methods and tools for ethically aligned design.

Continuous experimentation on artificial intelligence software: a research agenda

A holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products is described and a research agenda with seven questions for future studies is proposed.

Time for AI (Ethics) Maturity Model Is Now

It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ethically Aligned Design: An Empirical Evaluation of the RESOLVEDD-Strategy in Software and Systems Development Context

A key finding from the study indicates that simply the presence of an ethical tool has an effect on ethical consideration, creating more responsibility even in instances where the use of the tool is not intrinsically motivated.

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.