Water Resources Management, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 11, 2025
Language: Английский
Diagnostics, Journal Year: 2024, Volume and Issue: 14(8), P. 848 - 848
Published: April 19, 2024
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects healthcare, particularly in the medical imaging field. This review focuses on recent developments application deep learning (DL) techniques to breast cancer imaging. DL models, a subset AI algorithms inspired by human brain architecture, have demonstrated remarkable success analyzing complex images, enhancing diagnostic precision, and streamlining workflows. models been applied diagnosis via mammography, ultrasonography, magnetic resonance Furthermore, DL-based radiomic approaches may play role risk assessment, prognosis prediction, therapeutic response monitoring. Nevertheless, several challenges limited widespread adoption clinical practice, emphasizing importance rigorous validation, interpretability, technical considerations when implementing solutions. By examining fundamental concepts synthesizing latest advancements trends, this narrative aims provide valuable up-to-date insights for radiologists seeking harness power care.
Language: Английский
Citations
28
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)
Published: July 26, 2024
Abstract As the range of decisions made by Artificial Intelligence (AI) expands, need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind specific outcomes complex and opaque financial models requires a thorough justification to improve risk assessment, minimise loss trust, promote more resilient trustworthy ecosystem. This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 2022 highlights empirical examples demonstrating XAI's potential benefits in industry. We classified according tasks addressed using XAI, variation XAI methods between applications tasks, development application new methods. most popular were credit management, stock price predictions, fraud detection. three commonly employed black-box techniques finance whose explainability was evaluated Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), Random Forest. Most examined publications utilise feature importance, Shapley additive explanations (SHAP), rule-based In addition, they employ frameworks that integrate multiple techniques. also concisely define existing challenges, requirements, unresolved issues applying sector.
Language: Английский
Citations
28
Humanities and Social Sciences Communications, Journal Year: 2024, Volume and Issue: 11(1)
Published: March 15, 2024
Abstract The purpose of this research is to identify and evaluate the technical, ethical regulatory challenges related use Artificial Intelligence (AI) in healthcare. potential applications AI healthcare seem limitless vary their nature scope, ranging from privacy, research, informed consent, patient autonomy, accountability, health equity, fairness, AI-based diagnostic algorithms care management through automation for specific manual activities reduce paperwork human error. main faced by states regulating were identified, especially legal voids complexities adequate regulation better transparency. A few recommendations made protect data, mitigate risks regulate more efficiently international cooperation adoption harmonized standards under World Health Organization (WHO) line with its constitutional mandate digital public health. European Union (EU) law can serve as a model guidance WHO reform International Regulations (IHR).
Language: Английский
Citations
25
Journal of Economy and Technology, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 1, 2024
Language: Английский
Citations
25
BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(2), P. 1097 - 1143
Published: April 16, 2024
Recent advances in the field of large language models (LLMs) underline their high potential for applications a variety sectors. Their use healthcare, particular, holds out promising prospects improving medical practices. As we highlight this paper, LLMs have demonstrated remarkable capabilities understanding and generation that could indeed be put to good field. We also present main architectures these models, such as GPT, Bloom, or LLaMA, composed billions parameters. then examine recent trends datasets used train models. classify them according different criteria, size, source, subject (patient records, scientific articles, etc.). mention help improve patient care, accelerate research, optimize efficiency healthcare systems assisted diagnosis. several technical ethical issues need resolved before can extensively Consequently, propose discussion offered by new generations linguistic limitations when deployed domain healthcare.
Language: Английский
Citations
24
Remote Sensing, Journal Year: 2024, Volume and Issue: 16(5), P. 858 - 858
Published: Feb. 29, 2024
Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images inventory preparation integrated four machine learning models (Random Forest: RF, Classification Regression Trees: CART, Support Vector Machine: SVM, Extreme Gradient Boosting: XGBoost) predict Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power distance from streams, roads, lithology, rainfall, land use/land cover, normalized vegetation index) were as conditioning factors. The dataset was divided into 70% 30% training validation purposes using popular library, scikit-learn (i.e., train_test_split) Python programming language. Additionally, area under curve (AUC) evaluate performance models. accuracy results showed that XGBoost predicted with AUC values 0.807, 0.780, 0.756, 0.727, respectively. However, RF model performed better at prediction compared other applied. As per model, 22.49%, 16.02%, 12.67%, 18.10%, 31.70% watershed are estimated being very low, moderate, high, highly susceptible flooding, Therefore, integration data could have promising predicting similar environments.
Language: Английский
Citations
23
Urban Informatics, Journal Year: 2024, Volume and Issue: 3(1)
Published: Oct. 14, 2024
Abstract The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch data-driven smart city applications for efficient sustainable urban management. Despite their effectiveness, these often rely on massive amounts high-dimensional multi-domain data monitoring characterizing different sub-systems, presenting challenges in application areas that are limited quality availability, as well costly efforts generating scenarios design alternatives. As an emerging research area deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated unique values content generation. This paper aims to explore innovative integration GenAI techniques twins address planning management built environments with focuses various such transportation, energy, water, building infrastructure. survey starts introduction cutting-edge generative AI models, Adversarial Networks (GAN), Variational Autoencoders (VAEs), Pre-trained Transformer (GPT), followed a scoping review existing science leverage intelligent autonomous capability facilitate research, operations, critical subsystems, holistic environment. Based review, we discuss potential opportunities technical strategies integrate into next-generation more intelligent, scalable, automated development
Language: Английский
Citations
23
Neurocomputing, Journal Year: 2024, Volume and Issue: 599, P. 128111 - 128111
Published: Sept. 1, 2024
Language: Английский
Citations
20
Information Fusion, Journal Year: 2024, Volume and Issue: 115, P. 102721 - 102721
Published: Oct. 9, 2024
Language: Английский
Citations
17
Conflict Resolution Quarterly, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 9, 2025
ABSTRACT The integration of artificial intelligence (AI) into arbitration marks a significant transformation in alternative dispute resolution, aiming to enhance efficiency, objectivity, and accessibility. Advanced AI systems now extend beyond administrative tasks analyze complex legal data, predict case outcomes, even generate arbitral awards. This evolution addresses the growing volume complexity international disputes, particularly commercial investment arbitration. However, adoption introduces profound ethical challenges. Key concerns include absence human judgment, potential biases embedded algorithms, opacity their decision‐making processes, accountability issues, data privacy risks. Critically, current frameworks such as New York Convention were not designed accommodate AI‐generated awards, raising questions about legitimacy, procedural fairness, enforceability. article explores these intersections, focusing on how impacts arbitration's efficiency challenges arising from integration, extent which existing Employing multidisciplinary approach that includes scholarship, studies, technological research, analysis examines practical implications specific enforcement concludes with recommendations for regulatory reforms hybrid AI‐human models balance benefits necessity oversight accountability.
Language: Английский
Citations
8