Artificial Intelligence Review最新文献

Advances in generative artificial intelligence (AI), such as recent developments in text, audio, and video production, have amplified societal concerns, with threat probabilities estimated between 5 and 50%. This manuscript undertakes a comprehensive study to understand the factors influencing AI development, focusing on the interplay between AI research, cinematographic representations, and regulatory policies. The study reveals a strong interaction between scientific advances and cultural representations, indicating shared concerns and themes across both domains. It also highlights broad support for ethical and responsible AI development, with temporal analyses showing the significant influence of films on public perception and slower growth in policy implementation relative to cultural diffusion. The findings discuss the presence of a Pygmalion effect, where cultural representations shape perceptions of AI, and a potential Golem effect, where increased regulation may limit the dangerous development of AI and its societal impact. The study underscores the importance of balanced and ethical AI development, requiring continued monitoring and careful management of the relationship between research, cultural representations, and regulatory frameworks.

The analysis and treatment of brain tumors are among the most difficult medical conditions. Brain tumors must be detected accurately and promptly to improve patient outcomes and plan effective treatments. Recently used advanced technologies such as artificial intelligence (AI) and machine learning (ML) have increased interest in applying AI to detect brain tumors. However, concerns have emerged regarding the reliability and transparency of AI models in medical settings, as their decision-making processes are often opaque and difficult to interpret. This research is unique in its focus on explainability in AI-based brain tumor detection, prioritizing confidence, safety, and clinical adoption over mere accuracy. It gives a thorough overview of XAI methodologies, problems, and uses, linking scientific advances to the needs of real-world healthcare. XAI is a sub-section of artificial intelligence that seeks to solve this problem by offering understandable and straightforward and providing explanations for the choices made by AI representations. Applications such as healthcare, where the interpretability of AI models is essential for guaranteeing patient safety and fostering confidence between medical professionals and AI systems, have seen the introduction of XAI-based procedures. This paper reviews recent advancements in XAI-based brain tumor detection, focusing on methods that provide justifications for AI model predictions. The study highlights the advantages of XAI in improving patient outcomes and supporting medical decision-making. The findings reveal that ResNet 18 performed better, with 94% training accuracy, 96.86% testing accuracy, low loss (0.012), and a rapid time ((sim 6text {s})). ResNet 50 was a little slower ((sim 13text {s})) but stable, with 92.86% test accuracy. DenseNet121 (Adam W) achieved the highest accuracy at 97.71%, but it was not consistent across all optimizers. ViT-GRU also got 97% accuracy with very little loss (0.008), although it took a long time to compute (around 49 s). On the other hand, VGG models (around 94% test accuracy) and MobileNetV2 (loss up to 6.024) were less reliable, even though they trained faster. Additionally, it explores various opportunities, challenges, and clinical applications. Based on these findings, this research offers a comprehensive analysis of XAI-based brain tumor detection and encourages further investigation in specific areas.

Recent advances in Large Language Models (LLMs) have revitalized interest in Agent-Based Models (ABMs) by enabling “generative” simulations, with agents that can plan, reason, and interact through natural language. These developments promise greater realism and expressive power, but also revive long-standing concerns over empirical grounding, calibration, and validation—issues that have historically limited the uptake of ABMs in the social sciences. This paper systematically reviews the emerging literature on generative ABMs to assess how these long-standing challenges are being addressed. We map domains of application, categorize reported validation practices, and assess their alignment with the stated modeling goals. Our review suggests that the use of LLMs may exacerbate rather than alleviate the challenge of validating ABMs, given their black-box structure, cultural biases, and stochastic outputs. While the need for validation is increasingly acknowledged, studies often rely on face-validity or outcome measures that are only loosely tied to underlying mechanisms. Generative ABMs thus occupy an ambiguous methodological space—lacking both the parsimony of formal models and the empirical validity of data-driven approaches—and their contribution to cumulative social-scientific knowledge hinges on resolving this tension.

Over the past few decades, credit scoring has become an important tool in the financial sector. It enables banks and financial institutions to assess the creditworthiness of individuals and reduce the risk of default. As a result of significant advances in artificial intelligence techniques. Machine learning (ML) has made it possible to improve credit scoring by distinguishing between people with good creditworthiness and those with poorer creditworthiness. In this article, we propose a systematic literature review of ML-based financial credit scoring methods published between 2018 and 2024. A total of 330 research papers were extracted from four different online databases and digital libraries. After the study selection procedure, 63 research papers were selected for this systematic review. This paper aims to identify the major ML methods used in credit scoring, assess their strengths and limitations, and highlight notable trends and advancements. In addition, the review addresses the critical challenges faced in the adoption of ML models for credit scoring. This study not only contributes to the understanding of effective ML techniques used for credit scoring but also guides future research by highlighting the promising avenues in ML-based credit scoring efforts.

In recent years, the intensity and frequency of fires have increased significantly, resulting in considerable damage to properties and the environment through wildfires, oil pipeline fires, hazardous gas emissions, and building fires. Effective fire management systems are essential for early detection, rapid response, and mitigation of fire impacts. To address this challenge, unmanned aerial vehicles (UAVs) integrated with advanced state-of-the-art deep learning techniques offer a transformative solution for real-time fire detection, monitoring, and response. As UAVs play an essential role in the detection, classification and segmentation of fire-affected regions, enhancing vision-based fire management through advanced computer vision and deep learning technologies. This comprehensive survey critically examines recent advancements in vision-based fire management systems enabled by autonomous UAVs. It explores how baseline deep learning models, including convolutional neural networks, attention mechanisms, YOLO variants, generative adversarial networks and transformers, enhance UAV capabilities for fire-related tasks. Unlike previous reviews that focus on conventional machine learning and general AI approaches, this survey emphasizes the unique advantages and applications of deep learning-driven UAV platforms in fire scenarios. It provides detailed insights into various architectures, performance and applications used in UAV-based fire management. Additionally, the paper provides detailed insights into the available fire datasets along with their download links and outlines critical challenges, including data imbalance, privacy concerns, and real-time processing limitations. Finally, the survey identifies promising future directions, including multimodal sensor fusion, lightweight neural network architectures optimized for UAV deployment, and vision-language models. By synthesizing current research and identifying future directions, this survey aims to support the development of robust, intelligent UAV-based solutions for next-generation fire management. Researchers and professionals can access the GitHub repository.

Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbolic models—a practice known as conceptual retrofitting. This survey cuts through this confusion by introducing a novel dual-paradigm framework that categorizes agentic systems into two distinct lineages: the symbolic/classical (relying on algorithmic planning and persistent state) and the neural/generative (leveraging stochastic generation and prompt-driven orchestration). Through a systematic PRISMA-based review of 90 studies (2018–2025), we provide a comprehensive analysis structured around this framework across three dimensions: (1) the theoretical foundations and architectural principles defining each paradigm; (2) domain-specific implementations in healthcare, finance, and robotics, demonstrating how application constraints dictate paradigm selection; and (3) paradigm-specific ethical and governance challenges, revealing divergent risks and mitigation strategies. Our analysis reveals that the choice of paradigm is strategic: symbolic systems dominate safety-critical domains (e.g., healthcare), while neural systems prevail in adaptive, data-rich environments (e.g., finance). Furthermore, we identify critical research gaps, including a significant deficit in governance models for symbolic systems and a pressing need for hybrid neuro-symbolic architectures. The findings culminate in a strategic roadmap arguing that the future of Agentic AI lies not in the dominance of one paradigm, but in their intentional integration to create systems that are both adaptable and reliable. This work provides the essential conceptual toolkit to guide future research, development, and policy toward robust and trustworthy hybrid intelligent systems.

The broad application of passive acoustic monitoring provides a critical data foundation for studying soundscape ecology, necessitating automated analysis methods to accurately extract ecological information from vast soundscape data. This review comprehensively and cohesively examines two predominant approaches in soundscape analysis: soundscape component recognition and acoustic indices methods. Focusing on machine learning (ML)-based analysis methods for bird diversity assessment over the past five years, this review surveys representative research within each category, outlining their respective strengths and limitations. This not only addresses the growing interest in this field but also identifies research gaps and poses key questions for future studies. The insights from this review are anticipated to significantly enhance the understanding of ML applications in soundscape analysis, guiding subsequent investigative efforts in this rapidly evolving discipline, and thereby better supporting long-term biodiversity monitoring and conservation initiatives.

Mental health (MeHE) is a fundamental dimension of human well-being that encompasses emotional, psychological, and social aspects. Effective MeHE management enables individuals to cope with stress, maintain healthy relationships, and achieve their personal and social goals. However, traditional approaches are often inadequate in addressing the multidimensional challenges of early detection, personalized interventions, and comprehensive MeHE education. Large Language Models offer a transformative approach to the field of MeHE. With the ability to process large and complex textual data, these models can identify behavioral patterns in patients’ responses, suggest personalized interventions, and improve access to MeHE resources. Despite these advances, significant challenges remain. Applying reinforcement learning techniques to MeHE applications necessitates addressing challenges such as model-driven bias, protecting sensitive information, and providing robust evidence of clinical performance. This review systematically examines the applications of large language models in MeHE, providing a comprehensive analysis of their capabilities and limitations. This study examined how large language models address existing challenges, including early diagnosis, personalized treatments, and effective public education. Findings show that large language models increased the accuracy of early diagnosis of mental disorders by 33%, the effectiveness of personalized treatment plans by 27%, and participation in MeHE education and awareness by 24%. Ultimately, this research underscores the pivotal role of large language models in promoting MeHE. By providing practical insights and suggesting strategies to overcome implementation challenges, this review lays the groundwork for developing innovative, effective, and equitable solutions in the field of MeHE.

Reliable 3D detection is fundamental to autonomous systems such as mobile robots, self-driving cars, and unmanned aerial vehicles (UAVs). To achieve this capability, researchers have developed and trained supervised networks, which require large amounts of diverse and precisely annotated data. Due to the complex, expensive, and time-consuming capturing and annotation process, synthetic dataset generation approaches have gained popularity over the last decade. With increasing computational resources and advances in simulation technologies, a variety of dataset generators have emerged. These methods rely on either traditional 3D modeling or neural image synthesis to generate data for specific scenarios or general-purpose 3D detection tasks. Their primary goal is to produce high-quality, annotated 3D datasets in an automated and scalable manner. In this review, we evaluate the extent to which state-of-the-art approaches fulfill this goal by introducing a categorization scheme and conducting a comprehensive analysis of both 3D modeling and neural synthesis methods. Our analysis includes techniques used to address the Sim-to-Real domain gap. Furthermore, we assess each method’s level of automation, prerequisites, and practical adoption. This review aims to guide the reader in selecting automated dataset generation workflows for specific detection problems. By considering dataset quality, prerequisites, and application scenarios, we offer practical insights into identifying suitable methods for diverse downstream tasks.

Generative Artificial Intelligence (GenAI) is rapidly redefining the landscape of work organizations and society at large. GenAI has rapidly evolved from rule-based symbolic systems ofThe 1940 s to advanced deep learning architectures capable of producing human-like content across modalities, such as text, images, audio, and video. This review focuses on current emerging trends, such as large concept models and critical comparisons of tools, including ChatGPT, Gemini, and Claude. This study synthesizes evidence of GenAI’s essential role across major industries, revealing transformative applications in the finance, cloud and IT, healthcare, education, and energy sectors. The paper also highlights the unique opportunities GenAI offers for start-ups, enabling agile projects to leverage cutting-edge technology for competitive advantage. However, the deployment of GenAI systems through edge devices also raises critical challenges related to ethics, transparency, bias, accountability, computational issues, and many more. To address these complexities, this paper examines emerging approaches such as AI agents, agentic AI, and multi-agent systems that aim to extend the functionality of GenAI through autonomy, goal-directed behavior, and collaborative intelligence. It discovers novel incorporations with agentic AI architecture, such as BabyAGI, and discusses emerging issues of coordination, hallucination, and security risks. The findings reveal persistent challenges related to scalability, interpretability, and regulatory compliance while identifying future research directions toward developing more sophisticated, ethical, and accessible GenAI systems that will continue to reshape technological landscapes and societal interactions. This systematic review informs researchers, academicians, data scientists, and developers about the latest advancements in GenAI and highlights its applications and role across various industries, as well as supporting practitioners and scholars in staying current with the rapidly evolving landscape of generative technologies.