相关论文: Blockchain-Enabled Explainable AI for Trusted Heal…
Since the inception of the Bitcoin technology, its underlying data structure--the blockchain--has garnered much attention due to properties such as decentralization, transparency, and immutability. These properties make blockchains suitable…
Verisign reported a 125 percent increase in data breaches within the healthcare sector in the United States during 2022, with 18.2 million patient records being impacted. Growing healthcare data volumes and diversification mean that medical…
Unmanned aerial vehicle (UAV) networks are increasingly deployed for complex missions, including disaster response, intelligent logistics, and environmental monitoring. These missions generally require coordinated collaboration among…
Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment…
The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI (XAI) framework. Our methodology addresses three fundamental challenges in deploying AI…
In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to…
The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI models presents significant challenges to their clinical…
The complex nature of disease mechanisms and the variability of patient symptoms pose significant challenges in developing effective diagnostic tools. Although machine learning (ML) has made substantial advances in medical diagnosis, the…
AI-generated health misinformation poses unprecedented threats to patient safety and healthcare system trust globally. This white paper presents an explainable AI framework developed through the EPSRC INDICATE project to combat medical…
Concerns regarding privacy and data security in conventional healthcare prompted alternative technologies. In smart healthcare, blockchain technology addresses existing concerns with security, privacy, and electronic healthcare…
Artificial intelligence (AI) systems utilizing deep neural networks (DNNs) and machine learning (ML) algorithms are widely used for solving important problems in bioinformatics, biomedical informatics, and precision medicine. However,…
With the advent of blockchain technology, multiple research avenues and platforms for dialogue have opened up. However technology transfer to the pubic has not been implemented, such that regular public can access and make use of secure and…
Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques…
Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque "black boxes". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream…
Blockchain technology has emerged as a transformative paradigm for decentralized and secure data management across diverse application domains, including healthcare, supply chain management, and the Internet of Things. Its core features,…
Uncertainty is a fundamental challenge in medical practice, but current medical AI systems fail to explicitly quantify or communicate uncertainty in a way that aligns with clinical reasoning. Existing XAI works focus on interpreting model…
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explainable AI and its promise to render AI devices more…
Despite promising developments in Explainable Artificial Intelligence, the practical value of XAI methods remains under-explored and insufficiently validated in real-world settings. Robust and context-aware evaluation is essential, not only…
Protected Health Information (PHI) sharing significantly enhances patient care quality and coordination, contributing to more accurate diagnoses, efficient treatment plans, and a comprehensive understanding of patient history. Compliance…
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, with the aim of improving newborn care. The proposed…