Results 111 to 120 of about 25,088 (256)

The transformative potential of artificial intelligence in pediatric medicine: Current applications, methodological challenges, and future directions

Pediatric Investigation, EarlyView.
Artificial intelligence (AI) offers transformative potential for paediatric diagnosis and treatment, yet implementation faces unique challenges, including data scarcity, algorithmic bias, and children's developmental physiology. This review examines current applications and charts a path toward transparent, equitable, and trustworthy AI in child health.
Ruisong Wang, Xiaoman Ding, Wanyue Zhang, Tieliu Shi   +3 more
wiley   +1 more source

Machine learning‐based predictive models versus traditional risk scores in hemodialysis patients with comorbid urolithiasis

Precision Medical Sciences, EarlyView.
Machine learning‐based predictive models outperform traditional risk scores in hemodialysis patients with comorbid urolithiasis by capturing nonlinear, dialysis‐specific interactions. These approaches enable more accurate prediction of stone recurrence, sepsis, hospitalization, and mortality, supporting personalized risk stratification and precision ...
Dipal Chaulagain, Muhammad Riaz, Mirza Muhammad Hadeed Khawar, Jawad Hameed, Malik Sohail Riaz   +4 more
wiley   +1 more source

Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems [PDF]

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until
Allen, B. Danette
core   +1 more source

eXplainable Artificial Intelligence (XAI) in aging clock models

Ageing Research Reviews
eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems.
Alena Kalyakulina, Igor Yusipov, Alexey Moskalev, Claudio Franceschi, Mikhail Ivanchenko   +4 more
openaire   +3 more sources

DARPA's Explainable Artificial Intelligence Program

AI Magazine, 2019
Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain their decisions and actions to human users. DARPA's explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and ...
David Gunning, David W. Aha
openaire   +2 more sources

How AI and Digital Technologies Can Enhance Sustainable Livestock Manure Management: An Overview From Treatment to Distribution

Sustainable Development, EarlyView.
ABSTRACT Sustainable livestock manure management sits at the nexus of climate, nutrient circularity and water quality. This review explores how artificial intelligence (AI) and digital platforms are used across four management stages, that is, treatment, storage, valorisation and distribution, and figures out where integration fails to deliver ...
Zhan Shi, Feihong Liang, Francesco Marinello, Andrea Pezzuolo   +3 more
wiley   +1 more source

Scene-centric Joint Parsing of Cross-view Videos

, 2018
Cross-view video understanding is an important yet under-explored area in computer vision. In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene ...
Qi, Hang, Wu, Tianfu, Xu, Yuanlu, Yuan, Tao, Zhu, Song-Chun   +4 more
core   +1 more source

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

Sustainable Development, EarlyView.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella, Izni Zahidi, Chow Ming Fai, Chukwuma Okolie, Quoc Bao Pham   +4 more
wiley   +1 more source

Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging

Journal of Mathematical Sciences and Modelling
The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like ...
Cem Özkurt
doaj   +1 more source

AI‐Driven Circular Construction Waste Management for Advancing Sustainable Development

Sustainable Development, EarlyView.
ABSTRACT Construction and demolition waste (C&DW) represents up to 40% of global solid waste, posing a significant barrier to achieving circular economy (CE) objectives and the Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 12. However, construction waste management (CWM) systems remain constrained by fragmented data environments ...
Mohamed T. Elnabwy, Pablo Martinez
wiley   +1 more source