Results 121 to 130 of about 24,617 (284)
ABSTRACT Objective To provide an overview of potential biases resulting from the utilization of artificial intelligence (AI) in otolaryngology and techniques to mitigate them. Data Sources Literature review and expert opinion. Conclusions AI promises to fundamentally transform medicine.
Matthew T. Ryan, David A. Gudis
wiley +1 more source
Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging
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
ABSTRACT Objective To provide a comprehensive review of the current landscape of artificial intelligence (AI) applications in voice disorder, with emphasis on emerging applications, limitations, and future directions for clinical integration. Methods Literature review.
Rachel B. Kutler, Anaïs Rameau
wiley +1 more source
A Probability‐Aware AI Framework for Reliable Anti‐Jamming Communication
ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐hopping techniques. Nevertheless, these techniques become vulnerable when facing intelligent jammers.
Tawfeeq Shawly, Ahmed A. Alsheikhy
wiley +1 more source
Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria.
Gabriel Marín Díaz
doaj +1 more source
ABSTRACT Can AI‐driven capitalism sustain the moral preconditions of market order? We stage a dialogue between Adam Smith and a steel‐manned “EconAI” to test four Moral‐Market‐Fitness criteria: trustworthiness, fairness, non‐domination, and contestability, across 11 dilemmas.
Alexandra‐Codruța Bîzoi +1 more
wiley +1 more source
Open and Extensible Benchmark for Explainable Artificial Intelligence Methods
The interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues.
Ilia Moiseev +2 more
doaj +1 more source
Unveiling the factors of aesthetic preferences with explainable AI
Abstract The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspective by utilizing several different machine learning (ML) models that focus on aesthetic attributes known to influence preferences.
Derya Soydaner, Johan Wagemans
wiley +1 more source
Advances in Artificial Intelligence (AI) have sparked concerns regarding the transparency of model outputs, necessitating the development of eXplainable Artificial Intelligence (XAI) techniques.
Juliana da C. Feitosa +6 more
doaj +1 more source
Abstract Explainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. Here, we examined human participants' attention strategies when classifying images and when explaining how they classified the images through eye‐tracking and compared their attention strategies ...
Ruoxi Qi +4 more
wiley +1 more source

