Results 61 to 70 of about 27,865 (256)

A maize-centric framework for explainable artificial intelligence in decoding drought tolerance mechanisms

Discover Plants
Climate change-induced drought threatens global food security, with reports indicating that maize yield losses can exceed 30% in vulnerable regions, such as sub-Saharan Africa and South Asia.
Bushra Quyoom, Aijaz Ahmad Wani, Ajaz Ahmad Lone, Zahoor Ahmad Dar, Bilal Ahmad Mir, Latif Ahmad Peer   +5 more
doaj   +1 more source

Mapping the Research Landscape of Artificial Intelligence Innovation for Dropout Prevention and Learning Opportunity Loss Mitigation Policy

Jurnal Kependidikan
This study aims to map the research landscape of Explainable Artificial Intelligence (XAI) for dropout prevention and learning loss mitigation. The study employed bibliometric science mapping to identify publication trends, conceptual structures, and ...
Iik Nurulpaik, Ahmad Satibi, Encep Supriatna, Oki Suprianto   +3 more
doaj   +1 more source

Integrating Time‐Adjusted Imaging Instability Into Functional Outcome Prediction After Intracerebral Hemorrhage: Development and Validation of the HAGIV Score

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Early risk stratification may support clinical decision‐making in spontaneous intracerebral hemorrhage (ICH). We aimed to develop and internally validate HAGIV, a score integrating frequency of imaging markers (FIM), a time‐adjusted non‐contrast computed tomography (CT) metric of hematoma expansion, with established predictors for 90‐
Lei Song, Ren Ke, Anqi Chen, Yufei Fu, Hang Zhou, Rujia Wang, Liwei Zou, Yongqiang Yu, Ming Yuan, Xiaoming Qiu, Mengzhou Xue   +10 more
wiley   +1 more source

A Trust-Centered Explainable Deep-Learning Framework for Acute Lymphoblastic Leukemia Detection Using Multi-Model Fusion and Interpretability Scoring

Algorithms
Currently, advances in healthcare technologies are transforming medical diagnostics, particularly for data-driven disease detection. Acute lymphoblastic leukemia is a common and life-threatening blood cancer, especially prevalent in children.
Khadija Parwez, Syed Irfan Sohail, Muhammad Bilal, Wesam Shishah, Ali Raza, Mubeen Javed   +5 more
doaj   +1 more source

Explainable Artificial Intelligence for Diabetes Diagnosis [PDF]

E3S Web of Conferences
Whether young, old, type 1, type 2, gestational, newly diagnosed, long-time sufferer, caretaker or loved one, millions of people are afflicted and affected by diabetes.
Lamri Mohamed, Sahour Abdelhakim, Boumehrez Farouk, Bekhouche Abdelaali   +3 more
doaj   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Causal Inference

Engineering, 2020
Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms ...
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang   +9 more
doaj   +1 more source

Artificial Intelligence–Based Online Symptom Assessment Tools for Systemic Lupus Erythematosus Diagnosis: Patient Perspectives

Arthritis Care &Research, EarlyView.
Objective The objective of this article is to identify perceptions of patients with systemic lupus erythematosus (SLE) regarding artificial intelligence (AI)–based online symptom assessment tools, and the potential of these tools to address diagnostic barriers.
Olivia A. Stein, Jennifer L. F. Lee, Evelyne Vinet, Arielle Mendel, Christian A. Pineau, Fares Kalache, Louis‐Pierre Grenier, Sasha Bernatsky   +7 more
wiley   +1 more source

Explainable Artificial Intelligence: a Systematic Review

CoRR, 2020
Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability.
Vilone, G., Longo, L.
openaire   +3 more sources

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, EarlyView.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source