Artificial Intelligence Review最新文献

{"title":"Applications of machine learning for computer-aided diagnosis of Parkinson’s disease: progress and benchmark case study","authors":"Juntao Zhang,&nbsp;Yiming Zhang,&nbsp;Ying Weng,&nbsp;Akram A. Hosseini,&nbsp;Boding Wang,&nbsp;Tom Dening,&nbsp;Weinyu Fan,&nbsp;Weizhong Xiao","doi":"10.1007/s10462-025-11347-y","DOIUrl":"10.1007/s10462-025-11347-y","url":null,"abstract":"<div><p>Machine learning (ML) has emerged as a vital tool for the diagnosis of Parkinson’s Disease (PD). This study presents a comprehensive review on the applications of ML for computer-aided diagnosis (CAD) of PD. We conducted a comprehensive review by searching articles published from 2010 till 2024. The risk of bias is assessed using the PROBAST checklist. Case studies are also provided. This review includes 117 articles with six categories: neuroimaging data (20.5%); voice data (40.2%); handwriting data (12.0%); gait data (14.5%); EEG data (8.5%); and other data (4.3%). According to the PROBAST checklist, only 28 articles (23.9%) have a low risk of bias. A benchmark case study is conducted for five different data modalities. We also discuss current limitations and future directions of applying ML to the diagnosis of PD. This review reduces the gap between Artificial Intelligence (AI) and PD medical professionals and provides helpful information for future research.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11347-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond","authors":"Chiara Natali,&nbsp;Luca Marconi,&nbsp;Leslye Denisse Dias Duran,&nbsp;Federico Cabitza","doi":"10.1007/s10462-025-11352-1","DOIUrl":"10.1007/s10462-025-11352-1","url":null,"abstract":"<div><p>The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise and the reduction of opportunities for skill acquisition due to AI-driven decision support systems. Anchoring the discussion in the core medical competencies outlined by the <i>Federation of Royal Colleges of Physicians of the UK-Practical Assessment of Clinical Examination Skills</i> (PACES-MRCPUK), the systematic review identifies key vulnerabilities in physical examination, differential diagnosis, clinical judgment, and physician-patient communication. The narrative review explores broader themes related to Human–AI Interaction and the Impact of AI on Human Skills in Organizations. In response to concerns about the <i>Second Singularity</i>-a scenario in which decision-making autonomy is increasingly ceded to AI, weakening human oversight-this review advocates for a research agenda that prioritizes longitudinal studies, real-time monitoring of AI’s impact, and the development of frameworks to mitigate skill erosion, ensuring the preservation of professional autonomy and the safeguarding of the irreplaceable elements of human judgment in medicine and beyond.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11352-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ai-enabled framework for anomaly detection in power distribution networks under false data injection attacks","authors":"Hasnain Ahmad,&nbsp;Ghulam Mustafa,&nbsp;Muhammad Majid Gulzar,&nbsp;Ijaz Ahmed,&nbsp;Muhammad Khalid","doi":"10.1007/s10462-025-11318-3","DOIUrl":"10.1007/s10462-025-11318-3","url":null,"abstract":"<div><p>Modern power distribution networks are becoming cyber physical systems due to the addition of more advanced metering infrastructure (AMI). This has introduced new vulnerabilities to cyber threats, particularly false data injection (FDI) attacks. These attacks compromise the integrity of power consumption data, leading to financial losses, operational inefficiencies, and grid instability. Rule-based techniques and traditional machine learning models are two examples of traditional anomaly detection methods that often have problems. Often, these methods generate an excessive number of false alarms, struggle to adapt to new attack patterns, and perform poorly in large-scale deployments. This research suggests a robust anomaly identification framework (AIF) that uses an autoencoder (AE) for feature transformation and a multi-layer perceptron (MLP) to identify anomalies in AMI integrated with smart grids. The proposed approach first applies synthetic features extraction inspired by real-world smart meter capabilities and transforms the dataset using a denoising AE. MLP assisted in the classification to detect multiple FDI attack types with improved accuracy and reliability. Numerous experiments have been performed, and the results indicate that the suggested method works better than popular methods like correlation analysis, techniques based on clustering, and standard outlier identification algorithms. Compared to baseline methods, the proposed technique improves detection accuracy by up to approximately 25%, reduces false positives, and enhances the system’s ability to generalize across different cyberattack strategies. The proposed work computes seven different types of criterion matrices to verify the effectiveness of finding anomalies. The overall average results include mean squared error (0.0793), accuracy (92%), F1-Score (92%), recall (91%), specificity (94%), area under the curve (97%), and mean average precision (96%). These findings accentuate the potential of the proposed AIF performance in fortifying smart grid cybersecurity.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11318-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-agent reinforcement learning for resources allocation optimization: a survey","authors":"Mohamad A. Hady,&nbsp;Siyi Hu,&nbsp;Mahardhika Pratama,&nbsp;Zehong Cao,&nbsp;Ryszard Kowalczyk","doi":"10.1007/s10462-025-11340-5","DOIUrl":"10.1007/s10462-025-11340-5","url":null,"abstract":"<div><p>Multi-Agent Reinforcement Learning (MARL) has become a powerful framework for numerous real-world applications, modeling distributed decision-making and learning from interactions with complex environments. Resource Allocation Optimization (RAO) benefits significantly from MARL’s ability to tackle dynamic and decentralized contexts. MARL-based approaches are increasingly applied to RAO challenges across sectors playing a pivotal role in industry 4.0 developments. This survey provides a comprehensive review of recent MARL algorithms for RAO, encompassing core concepts, classifications, design steps and benchmarks. By outlining the current research landscape and identifying primary challenges and future directions, this survey aims to support researchers and practitioners in leveraging MARL’s potential to advance resource allocation solutions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11340-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SFI-ensemble: Sugeno fuzzy integral-based ensemble of CNN models with meta-heuristic fuzzy measures for mouth and oral disease detection","authors":"Sohaib Asif,&nbsp;Shasha Chen,&nbsp;Yajun Ying,&nbsp;Changfu Zheng,&nbsp;Vicky Yang Wang,&nbsp;Enyu Wang,&nbsp;Dong Xu","doi":"10.1007/s10462-025-11345-0","DOIUrl":"10.1007/s10462-025-11345-0","url":null,"abstract":"<div><p>The rising prevalence of mouth and oral diseases (MOD), including gum disease and oral cancer, presents a major global health challenge, where timely detection is crucial for effective intervention. Recognizing the limitations of a single learning model in capturing intricate information for precise disease prediction from complex data, we introduce a robust deep learning ensemble framework named Sugeno Fuzzy Integral (SFI)-Ensemble in this paper. Our methodology involves a meticulous preprocessing of the dataset utilizing fuzzy contrast enhancement (FCE) to enhance data quality and contrast. Additionally, we propose a reconstruction approach that employs transfer learning (TL) and fine-tuning on four Convolutional Neural Network (CNN) models—DenseNet121, MobileNetV1, DenseNet169, and Resnet101V2—to optimize their architectures specifically for MOD classification. The focal point of this contribution lies in the introduction of a groundbreaking ensemble method. This ensemble method dynamically combines decision scores from the CNN models using the SFI-based technique, offering a resilient and adaptive approach that factors in the confidence of base learners’ predictions through fuzzy integrals. To overcome the prevalent challenge of experimentally defining fuzzy measures in ensemble methods based on fuzzy integrals, we surpass conventional manual tuning. Our approach involves the utilization of seven distinct meta-heuristic optimization algorithms for the optimal determination of fuzzy measures. This not only ensures stability but also highlights the effectiveness of the proposed SFI-Ensemble. A comprehensive assessment is carried out on publicly accessible datasets to detect MOD, complemented by Grad-CAM interpretability and meticulous statistical analyses. Additionally, we benchmark the results against baseline models and state-of-the-art methods, with our proposed framework consistently surpassing them, attaining an impressive accuracy of 99.70%. This underscores the superior performance and robustness of our proposed methodology in contrast to traditional ensemble methods. Our approach, integrating dataset preprocessing, model reconstruction, and ensemble innovation, provides doctors with an effective tool for accurate MOD diagnosis, enhancing adaptability and performance through fuzzy integral-based fusion.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11345-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy and security in recommenders: an analytical review","authors":"Bolanle Adefowoke Ojokoh,&nbsp;Folasade Olubusola Isinkaye,&nbsp;Ming Zhang,&nbsp;Joshua Joshua Tom,&nbsp;Arome Junior Gabriel,&nbsp;Olaitan Afolabi,&nbsp;Bamidele Afolabi","doi":"10.1007/s10462-025-11333-4","DOIUrl":"10.1007/s10462-025-11333-4","url":null,"abstract":"<div><p>Recommender systems (RSs) effectively curb information overload by providing personalized suggestions of items to users across different online domains. Their widespread use in e-commerce enhances user engagement, personalizes shopping experiences, and drives sales growth. However, despite the effectiveness of these systems at generating recommendations for users, they still raise major privacy and security concerns as their data could be exploited for malicious purposes, which can lead to data breaches and misuse. Therefore, this paper presents a comprehensive and systematic review of the underlying causes of privacy and security challenges in RS. It also provides a detailed taxonomy categorizing these concerns based on their targets and the risks they create. It further presents potential solutions that have been used in the literature while identifying challenges and possible research directions to pursue in a bid to address privacy and security concerns in RSs. This paper will be a useful resource for current and upcoming researchers in the domain of RSs. It will support knowledge advancement and steer appropriate research directions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11333-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"(Ir)rationality in AI: state of the art, research challenges and open questions","authors":"Olivia Macmillan-Scott,&nbsp;Mirco Musolesi","doi":"10.1007/s10462-025-11341-4","DOIUrl":"10.1007/s10462-025-11341-4","url":null,"abstract":"<div><p>The concept of rationality is central to the field of artificial intelligence (AI). Whether we are seeking to simulate human reasoning, or trying to achieve bounded optimality, our goal is generally to make artificial agents as rational as possible. Despite the centrality of the concept within AI, there is no unified definition of what constitutes a rational agent. This article provides a survey of rationality and irrationality in AI, and sets out the open questions in this area. We consider how the understanding of rationality in other fields has influenced its conception within AI, in particular work in economics, philosophy and psychology. Focusing on the behaviour of artificial agents, we examine irrational behaviours that can prove to be optimal in certain scenarios. Some methods have been developed to deal with irrational agents, both in terms of identification and interaction, however work in this area remains limited. Methods that have up to now been developed for other purposes, namely adversarial scenarios, may be adapted to suit interactions with artificial agents. We further discuss the interplay between human and artificial agents, and the role that rationality plays within this interaction; many questions remain in this area, relating to potentially irrational behaviour of both humans and artificial agents.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11341-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inclusive prompt engineering for large language models: a modular framework for ethical, structured, and adaptive AI","authors":"Mohamad Saleh Torkestani,&nbsp;Ali Alameer,&nbsp;Shivakumara Palaiahnakote,&nbsp;Taha Manosuri","doi":"10.1007/s10462-025-11330-7","DOIUrl":"10.1007/s10462-025-11330-7","url":null,"abstract":"<div><p>Large language models have achieved impressive results across various tasks but remain limited in their ability to adapt ethically and structurally across diverse domains without retraining. This paper presents the Inclusive Prompt Engineering Model (IPEM), a modular framework designed to enhance LLM performance, adaptability, and ethical alignment through prompt-level strategies alone. IPEM integrates four components: Memory-of-Thought for multi-turn consistency, Enhanced Chain-of-Thought prompting for logical verification, Structured and Analogical Reasoning modules for tabular and cross-domain tasks, and Evaluation and Feedback Loops that incorporate uncertainty-aware selection and bias mitigation mechanisms. Evaluated across tasks in arithmetic reasoning, healthcare triage, financial forecasting, and inclusive question answering, IPEM consistently improves model outputs over a GPT-4 baseline. Notable outcomes include up to twenty percentage points in accuracy gains, a 25 percent reduction in logical errors, and nearly 20 percent reduction in social bias scores, all without modifying model weights. Moreover, IPEM reduces annotation demands by one-third while preserving performance, demonstrating its utility in low-resource environments. By unifying ethical safeguards and reasoning mechanisms in a prompt-based system, IPEM offers a reproducible and auditable pathway for deploying adaptable and fair AI systems. The framework contributes both practical solutions and theoretical insights to the evolving field of prompt engineering.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11330-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oriented object detection in optical remote sensing images using deep learning: a survey","authors":"Kun Wang,&nbsp;Zi Wang,&nbsp;Zhang Li,&nbsp;Ang Su,&nbsp;Xichao Teng,&nbsp;Erting Pan,&nbsp;Minhao Liu,&nbsp;Qifeng Yu","doi":"10.1007/s10462-025-11256-0","DOIUrl":"10.1007/s10462-025-11256-0","url":null,"abstract":"<div><p>Oriented object detection is a fundamental yet challenging task in remote sensing (RS), aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection methods. Given the rapid development of this field, a comprehensive survey of the recent advances in oriented object detection is presented in this paper. Specifically, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific related challenges, including feature misalignment, spatial misalignment, oriented bounding box (OBB) regression problems, and common issues encountered in RS. Subsequently, we further categorize the existing methods into detection frameworks, OBB regression techniques, feature representation approaches, and solutions to common issues and provide an in-depth discussion of how these methods address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis involving the state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection research.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11256-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for secure and sustainable industrial control systems - A Survey of challenges and solutions","authors":"Muhammad Muzamil Aslam,&nbsp;Ali Tufail,&nbsp;Haji Gul,&nbsp;Muhammad Nauman Irshad,&nbsp;Abdallah Namoun","doi":"10.1007/s10462-025-11320-9","DOIUrl":"10.1007/s10462-025-11320-9","url":null,"abstract":"<div><p>In modern industrial environments, the security and sustainability of Industrial Control Systems (ICS) have become crucial. This comprehensive review examines the transformative potential of Artificial Intelligence (AI) in ICS, focusing on technologies like Machine Learning (ML), Deep Learning (DL), Large Language Models (LLMs), and cloud computing. Moreover, this research explores integrating existing and proposed sustainable practices within the ICS framework, with a particular emphasis on energy efficiency and carbon footprint reduction, to enhance the overall sustainability of ICS. This review employed a systematic approach to select relevant articles from multiple reputable databases, such as Scopus, IEEE Explore, Science Direct, ACM digital library, Web of Science, and IET digital library, including 250 articles that provide valuable insights into the intersection of AI, security, and sustainability in ICS. This review examines vulnerabilities in ICS, such as data breaches, insider threats, and malware, emphasizing the need for effective anomaly detection. It highlights how AI technologies like anomaly detection and predictive analytics can enhance threat detection and response in ICS by improving accuracy and efficiency. The review offers insights to researchers and professionals on the future of secure, sustainable ICS, supporting a resilient industrial landscape that meets cybersecurity, compliance, and sustainability goals.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11320-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}