Results 121 to 130 of about 28,332 (249)

Current Standards of Monitoring Models in Healthcare Settings

Advanced Intelligent Discovery, EarlyView.
AI/ML‐enabled medical devices are entering clinical practice faster than monitoring standards mature. This review highlights gaps in postmarket surveillance, limited use of predetermined change‐control plans, and the need for ongoing performance tracking, drift detection, explainability, and workflow‐aware governance to support safer, more reliable ...
Alan Kay, Daljit Takher, Wenting Liu, Emily Wittrup, Gilbert S. Omenn, Kayvan Najarian   +5 more
wiley   +1 more source

A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study. [PDF]

PLoS One, 2022
Jafari H, Shohaimi S, Salari N, Kiaei AA, Najafi F, Khazaei S, Niaparast M, Abdollahi A, Mohammadi M.   +8 more
europepmc   +1 more source

Multicenter Validation of Machine Learning Models for Predicting Myopia Progression Across School‐Based Screening and Optical Intervention Settings in Chinese Children and Adolescents

Advanced Intelligent Discovery, EarlyView.
Machine‐learning models predict myopia trajectories from 12 routine ocular variables in 40 445 children across 10 hospitals. Externally validated across school‐based screening and optical‐intervention settings, the best models achieve AUROC 0.97 for myopia status and R2 0.83 for spherical equivalent, enabling personalized risk stratification to curb ...
Yitong Zhang, Mingxuan Liu, Yifei Chen, Ying Wu, Lijun Zhang, Mengyao Guo, Yulan Yan, Runmei Wei, Zihan Cui, Peng Luo, Fang Chen, Bo Yang, Youcang Han, Xiaoyang Liu, Qiyuan Tian   +14 more
wiley   +1 more source

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values. [PDF]

PLoS Comput Biol, 2021
Cavallaro M, Moiz H, Keeling MJ, McCarthy ND.   +3 more
europepmc   +1 more source

Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science

Advanced Intelligent Systems, EarlyView.
To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska, Joanna Neubauer, Monika Sztuder, Witold Taisner, Agnieszka Hryniewicka, Julia Kolodziejczyk, Marta Eliza Plonska‐Brzezinska, Dariusz Brzezinski   +7 more
wiley   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Integration of Liquid Handling Robotics, Turbidimetry, and Machine Learning Toward Inverse Design of Lower Critical Solution Temperature Polymer Formulations

Advanced Intelligent Systems, EarlyView.
An integrated OT‐2/plate reader platform automates LCST cloud point measurement of NIPAM‐co‐DMAEMA copolymers across high‐dimensional formulation spaces. Machine learning trained on the resulting high‐throughput datasets achieves R2 up to 0.96 and enables inverse design of polymer formulations to target transition temperatures with <1% error.
Lachlan Alexander, Vianna F. Jafari, Tanja Junkers   +2 more
wiley   +1 more source

Responsible Artificial Intelligence in Courts: A Four‐Test Framework

AI &Innovation, EarlyView.
ABSTRACT A structured framework for responsible AI applications relating to judicial decision‐making and the adjudicative functions of courts requires the satisfaction of multiple context‐specific safeguards. This article proposes a four‐test framework designed to evaluate whether AI systems used in courts operate in accordance with legal, procedural ...
Kwan Yiu Cheng
wiley   +1 more source