Artificial Intelligence Systems Research Articles

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray

PurposePneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on chest radiography (CXR), and it presents a setting where artificial intelligence (AI) methods could find terrain in aiding radiologists in facing increasing workloads. Hence, the purpose of our study was to test an AI system for the detection of PTX on CXR examinations, to review its diagnostic performance in such setting alongside that of reading radiologists. MethodWe retrospectively ran the AI system on CXR examinations of patients who were imaged for the suspicion of PTX, and who also underwent computed tomography (CT) within the same day, the latter being used as reference standard. The performance of the proposed AI system was compared to that of reading radiologists, obtained from CXR reports. ResultsOverall, the AI system achieved an accuracy of 74 % (95%CI 68–79 %), with a sensitivity of 66 % (95%CI 59–73 %) and a specificity of 93 % (95%CI 85–97 %). Human readers displayed a comparable accuracy (77 %, 95%CI 71–82 %, p = 0.355), with higher sensitivity (73 %, 95%CI 66–79 %, p = 0.040), albeit lower specificity (85 %, 95%CI 75–91 %, p = 0.034). The performance of AI was influenced by patient positioning at CXR (p = 0.040). ConclusionsThe proposed tool could represent an aid to radiologists in detecting PTX, improving specificity. Further improvement with training on more challenging cases may pave the way for its use as a screening or standalone tool.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.

Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines. Soon after, in the 1950s and 1960s, machine learning algorithms such as neural networks and decision trees ignited significant enthusiasm. More recent advancements include the refinement of learning algorithms, the development of convolutional neural networks to efficiently analyze images, and methods to synthesize new images. This renewed enthusiasm was also due to the increase in computational power with graphical processing units and the availability of large digital databases to be mined by neural networks. AI soon began to be applied in medicine, first through expert systems designed to support the clinician's decision and later with neural networks for the detection, classification, or segmentation of malignant lesions in medical images. A recent prospective clinical trial demonstrated the non-inferiority of AI alone compared with a double reading by two radiologists on screening mammography. Natural language processing, recurrent neural networks, transformers, and generative models have both improved the capabilities of making an automated reading of medical images and moved AI to new domains, including the text analysis of electronic health records, image self-labeling, and self-reporting. The availability of open-source and free libraries, as well as powerful computing resources, has greatly facilitated the adoption of deep learning by researchers and clinicians. Key concerns surrounding AI in healthcare include the need for clinical trials to demonstrate efficacy, the perception of AI tools as 'black boxes' that require greater interpretability and explainability, and ethical issues related to ensuring fairness and trustworthiness in AI systems. Thanks to its versatility and impressive results, AI is one of the most promising resources for frontier research and applications in medicine, in particular for oncological applications.

Application of artificial intelligence in career development of women in STEM field: Counseling, network construction and recruitment

With the rapid development of science and technology, the application of artificial intelligence (AI) in STEM (science, technology, engineering and mathematics) is increasingly extensive, which provides a new idea for solving the challenges of women's career development in STEM. This paper deeply discusses the application of AI in counseling, network construction and recruitment of women's STEM career development, aiming at promoting gender equality and women's career growth. In career counseling, AI technology provides customized career advice and development path for women through deep learning and big data analysis. According to personal education background, skill level, hobbies and career goals, the system intelligently recommends suitable career directions, and updates industry trends and job market data in real time to help women make more informed career choices. The AI tutoring system also enhances women's professional self-confidence through functions such as simulated interviews and career planning exercises. In terms of network construction, AI helps women to expand their professional networks more effectively through intelligent recommendation system. Based on user behavior data and preferences, AI accurately recommends industry seminars, technical exchange activities and online communities to promote women to establish contacts with like-minded professionals. AI also assists women in managing and maintaining network relationships, ensures the continuity of network interaction through intelligent reminding functions, and identifies key nodes and influential people to enhance network quality. In the recruitment process, AI technology reduces gender bias and improves the fairness and transparency of recruitment through automatic resume screening, standardized interview process and competency-based evaluation model. AI system can analyze a large amount of data, accurately match the skills and job requirements of female candidates, and improve the employment rate of women in STEM field. At the same time, through transparent algorithm decision-making process and fairness check, the potential gender bias problem in AI algorithm is overcome. The research in this paper provides theoretical support and practical guidance for using AI technology to promote women's career development in STEM field, and helps to build a more equal and inclusive scientific and technological working environment.

Bridging Human and Machine Intelligence: Reverse-Engineering Radiologist Intentions for Clinical Trust and Adoption

In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation heatmap videos and corresponding radiology reports. Additionally, the RE module extracts regions of interest that align with these intentions, mirroring the radiologist’s diagnostic focus. This approach introduces a new task, radiologist intention detection, and is the first application of Dense Video Captioning (DVC) in the medical domain. By making AI systems more interpretable and aligned with radiologist’s cognitive processes, this proposed system aims to enhance trust, improve diagnostic accuracy, and support medical education. Additionally, it holds the potential for automated error correction, guiding junior radiologists, and fostering more effective training and feedback mechanisms. This work sets a precedent for future research in AI-driven healthcare, offering a pathway towards transparent, trustworthy, and human-centered AI systems. We evaluated this model using NLG(Natural Language Generation), time-related, and vision-based metrics, demonstrating superior performance in generating temporally grounded intentions on REFLACX and EGD-CXR datasets. This model also demonstrated strong predictive accuracy in overlap scores for medical abnormalities and effective region extraction with high IoU(Intersection over Union), especially in complex cases like cardiomegaly and edema. These results highlight the system's potential to enhance diagnostic accuracy and support continuous learning in radiology. We are also releasing the source code for our project, available here.

Navigating IT Careers in the Age of AI : Human Skills in an Automated Future

This article investigates the current state of AI (Artificial Intelligence), its market, and the potential impacts of AI on future jobs. The AI revolution is not only transforming the labour market across the world but also augmenting business processes and procedures in various areas. We explore the evolving relationship between human workers and AI systems in IT by analysing current trends, industry reports, and expert predictions. Nature and Extent of jobs that are likely to be affected by Automation are also a part of the study. The article offers insights into the ever-changing nature of IT roles, identifies critical skills for future success, and presents recommendations for professionals seeking to thrive in an AI-augmented work environment.

AI and radiologist assessments on the prevalence of TMJ osteoarthritis using radiographic images: A comparative study

Objectives: This study aims to address the significant discomfort and functional impairment associated with temporomandibular joint osteoarthritis (TMJ OA), which negatively impacts the quality of life. It emphasizes the importance of prompt diagnosis and explores the potential of an Artificial Intelligence (AI) system to enhance TMJ OA diagnosis. Methods: The prevalence of TMJ OA was evaluated using 3 diagnostic tools: the gold standard, the AI model, and an examiner. In total, 132 patients who performed 190 cone-beam computed tomography (CBCT) images were included. Results: The prevalence of TMJ OA was 62.11% using the gold standard, 63.68% using the AI model, and 58.42% when assessed by the examiner. No gender variation in TMJ OA diagnosis was reported (p-value>0.05). Age variations were reported with the gold standard and the examiner diagnosis. When compared to the gold standard, the AI model had remarkable sensitivity (97.46%) and specificity (91.67%). Conclusions: The AI model shows promise in enhancing the accuracy of TMJ OA diagnosis, offering potential benefits for early detection and improved patient outcomes.

Requirements for the Concept of the Implementation of Artificial Intelligence as a Tool for Preventing Corruption in the Public Sector

Abstract Artificial intelligence (AI) technologies have enormous potential for preventing corruption in the public sector. However, at present, the use of AI in the public sector is not regulated. Consequently, the article aims to propose concrete steps and actions for the implementation of AI technology in the public sector with a view to using this technology as a new tool to identify and prevent situations of corruption. The author conducted a study of Latvian state and local government officials in different regions and positions throughout 2023. In the article, the author analyses the research data on officials’ experiences with AI, their expectations about the effectiveness of AI in preventing corruption, as well as their basic requirements for the concept of implementation of AI. The author of the article proposes to create a basis for the legal environment for the use of AI and recommends adopting the guidelines of AI on the following basic ethical standards: the priority of human well-being; prohibition of harm at the initiative of the AI system; human control; compliance of design with law; prevention of covert manipulation of human behaviour; built-in security. In the article, the author recommends creating a special procedure for testing and further implementation of solutions of AI, bypassing redundant administrative procedures. In addition, the author recommends stimulating anti-corruption institutions and developing approaches to issues of civil liability for mistakes made by AI technology.

AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information.

This study investigates the accuracy of Artificial Intelligence (AI) chatbots, ChatGPT and Bard, in providing information on Hidradenitis Suppurativa (HS), aiming to explore their potential in assisting HS patients by offering insights into symptoms, thus possibly reducing the diagnostic and treatment time gap. Using questions formulated with the help of HS patient associations, both ChatGPT and Bard were assessed. Responses to these questions were evaluated by 18hS experts. ChatGPT's responses were considered accurate in 86% of cases, significantly outperforming Bard, which only achieved 14% accuracy. Despite the general efficacy of ChatGPT in providing relevant information across a range of HS-related queries, both AI systems showed limitations in offering adequate advice on treatments. The study identifies a significant difference in the performance of the two AIs, emphasizing the need for improvement in AI-driven medical advice, particularly regarding treatment options. The study highlights the potential of AI chatbots, particularly ChatGPT, in supporting HS patients by improving symptom understanding and potentially reducing the time to diagnosis and treatment. AI chatbots, while promising, cannot yet substitute for professional medical diagnosis and treatment, indicating the importance of enhancing AI capabilities for more accurate and reliable medical information dissemination.

Ambiguous AI: Lossy Compression, Model Collapse, and Intercultural Sensitivity

This paper examines the intersections of artificial intelligence (AI), cultural representation, and intercultural sensitivity, critiquing the impact of lossy compression and model collapse on cultural expression in AI systems. The study introduces “intentional patiency” as an alternative framework, conceptualizing AI not merely as an active agent but as a cultural receptor, and in turn, producer. AI systems often rely on compression algorithms that strip away cultural complexities, rendering nuanced traditions as simplified, stereotypical artifacts. Drawing on frameworks from Ted Chiang’s metaphor of lossy compression to Wendy Chun’s critique of noise, the essay explores how AI’s data optimization selectively filters cultural diversity, perpetuating dominant narratives while silencing marginalized voices. Model collapse in generative systems demonstrates while also exacertabting the pitfalls of homogenous data to reduce cultural representations to monolithic “single stories,” echoing concerns raised by Chimamanda Ngozi Adichie. To address this issue, this essay advocates for an interculturally sensitive AI development paradigm, incorporating cultural expansion and ethical safeguards to mitigate cultural erasure. Through the concept of “ambiguous AI,” a paradigm rooted in probabilistic engagement and intentional patiency, the essay seeks to reframe AI’s role as a nuanced participant in cultural discourse, promoting culturally adaptive architectures.

Devising a method for integrated dataset formation and selecting a model for recognizing the technical condition of unmanned aerial vehicle

The object of this study is the process of forming a training dataset for diagnosing the technical condition of unmanned aerial vehicles (UAVs) using machine-learning algorithms. UAV flights are extremely important for various aspects of troop deployment. Combat UAV flights are performed under the influence of negative factors that cause flight special cases (FSC), which hinder the execution of combat missions, lead to mission failures, and result in the aircraft damage or loss. The available capabilities of autopilots are not enough for control under complex conditions, and in certain situations, the human operator cannot timely recognize a flight special case, including evaluation of the destructive impact of enemy’s electronic warfare systems on communication channels and operation of UAV. Therefore, the urgent issue is the intellectualization of onboard control systems, particularly towards recognizing the current technical state of UAV using artificial intelligence methods. To design such systems, labeled datasets are required. The procedure for forming datasets that consider the specificity of UAV construction and their combat use under adversarial conditions is not defined, necessitating the development of an appropriate method. Based on the well-known CRISP-DM methodology, a method for dataset formation has been proposed for subsequent use in artificial intelligence systems that use various machine-learning methods. This method differs from existing ones by considering the specificity of combat mission execution under adversarial conditions, which allowed for an 8.0 % increase in the accuracy of recognizing special cases in UAV flights by the onboard system. It also enabled timely detection of electronic warfare impacts on UAV and evaluation of the effectiveness of radio signal receivers jamming

Professionally significant qualities of teaching producing in the context of digital development: Evaluating the effectiveness by education stakeholders

Introduction. The article is devoted to the problem of increasing the level of communicative culture in future economists in the digital era. The purpose of the research is to substantiate the effectiveness of professionally important qualities of teaching producing in universities of Economics, taking into account the digital format of interaction. Materials and Methods. The methodological basis of the study is the Pareto optimality principle (2080), which allows the authors to determine the necessary minimum of teaching resources in terms of modern communication knowledge for training economists in the digital age. During the study, an online survey was conducted in order to substantiate the effectiveness of professionally important qualities of teaching producing in the context of the digital specifics of the socio-economic space. The research sample consisted of students and academic staff of economics departments of the Financial University under the Government of the Russian Federation, Omsk State Pedagogical University, and Omsk State Technical University. Results. The theoretical analysis of the research problem revealed professionally important qualities of teaching producing (communication skills, digitals kills, knowledge of the principles of teaching ethics to create an atmosphere of trust and psychological safety), which facilitate the development of future economists’ communicative culture (motivational, cognitive, and functional components). According to the results of the empirical part of the study, the education stakeholders highly appreciated the effectiveness of professionally significant qualities of teaching producing and their positive correlation. Conclusions. The use of the Pareto rule allows the authors to conclude that the formation of professionally significant t qualities of teaching producing, which make up about 20% of the professional qualities composition for academic staff at universities of economics, determines 80% of the productivity of educational outcomes in terms of developing future economists’ communicative culture. This, in turn, determines the ability of graduates to build communication taking into account the career prospects at various levels of digitalization and interaction with artificial intelligence systems.

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma

The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.

Enhancing IVF Outcomes with Artificial Intelligence: Current Advances and Future Possibilities

The integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.

Integration of Artificial Intelligence and Genome Editing System for Determining the Treatment of Genetic Disorders

Integration of Artificial Intelligence and Genome Editing System for Determining the Treatment of Genetic Disorders

Engineering Steep Subthreshold Swings in High-Performance Organic Field-Effect Transistor Sensors.

Organic field-effect transistor (OFET)-based sensors have gained considerable attention for information perception and processing in developing artificial intelligent systems owing to their amplification function and multiterminal regulation. Over the last few decades, extensive research has been conducted on developing OFETs with steep subthreshold swings (SS) to achieve high-performance sensing. In this review, based on an analysis of the critical factors that are unfavorable for a steep SS in OFETs, the corresponding representative strategies for achieving steep SS are summarized, and the advantages and limitations of these strategies are comprehensively discussed. Furthermore, a bridge between SS and OFET sensor performance is established. Subsequently, the applications of OFETs with steep SS in sensor systems, including pressure sensors, photosensors, biochemical sensors, and electrophysiological signal sensors. Lastly, the challenges faced in developing OFET sensors with steep SS are discussed. This study provides insights into the design and application of high-performance OFET sensor systems.

Visual Search Interactive Model for Artificial Intelligence Robotics Model forthe Agricultural Field Analysis

The Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced computer vision techniques and machine learning algorithms, this model enables AI systems to interpret and analyze visual information in real-time, facilitating tasks such as object recognition, image classification, and scene understanding. The interactive nature of the model allows users to engage with the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged 88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%, while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5 kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.

From Generative AI to Objective-Driven Systems: A Paradigm Shift in Artificial Intelligence

Marwan Omar 1Illinois Institute of Technology Email: momar3@iit.edu Abstract The rapid advancement of Artificial Intelligence (AI), particularly in the domain of generative models, has led to impressive achievements in content creation and natural language processing. However, these models are inherently limited by their reliance on pattern recognition and lack of true understanding. In contrast, Objective-Driven AI offers a promising alternative by focusing on goal-oriented behavior, causal reasoning, and the development of world models. This paper explores the limitations of generative AI, highlighting its inability to grasp context, causality, and ethical considerations. It then presents the concept of Objective-Driven AI, emphasizing its potential to operate effectively in complex, real-world environments where understanding and reasoning are critical. The paper concludes with a discussion of future research directions, including advanced world modeling techniques, ethical AI, and robustness against adversarial attacks, which are essential for the further development of Objective-Driven AI systems. Keywords Objective-Driven AI, Generative AI, Causal Reasoning, World Modeling, Ethical AI, Artificial Intelligence, Adversarial Attacks, Machine Learning, Autonomous Systems

How Explainable Artificial Intelligence Can Increase or Decrease Clinicians' Trust in AI Applications in Health Care: Systematic Review.

Artificial intelligence (AI) has significant potential in clinical practice. However, its "black box" nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians' trust in AI-driven clinical decision-making. A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians' trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians' trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians' trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice.

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Artificial Intelligence and Artificial Stupidity: The Inseparables

Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making. This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.