Development Of Artificial Intelligence Systems Research Articles

Implications of causality in artificial intelligence.

Over the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the 'black box' and bias in AI models. Several approaches have been developed to reduce these problems. Responsible AI focuses on the ethical development of AI systems, considering social impact. Fair AI seeks to identify and correct algorithm biases, promoting equitable decisions. Explainable AI aims to create transparent models that allow users to interpret results. Finally, Causal AI emphasizes identifying cause-and-effect relationships and plays a crucial role in creating more robust and reliable systems, thereby promoting fairness and transparency in AI development. Responsible, Fair, and Explainable AI has several weaknesses. However, Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI.

Enhancing autism rehabilitation: the role of expert systems in supporting therapy with intelligent robotics

Autism is a complex neurobiological disorder that affects brain development and manifests itself primarily in difficulties in communication and social interactions. Autistic people may also experience a restricted set of interests and repetitive behaviors. Among the promising tools supporting autistic people, systems supporting diagnosis, planning therapy, monitoring progress, and even conducting direct interactions with autistic children are becoming increasingly important. The article describes an expert system created to support the rehabilitation of children with autism. The presented expert system, for instance, can analyze the child's speech patterns and suggest appropriate communication strategies, or it can monitor the child's behavior and propose activities to address repetitive behaviors. The system is part of a more extensive solution consisting of three main components: the patient interface, the rehabilitator's expert system, and the machine learning module. The presented environment was used during therapeutic sessions with young patients. On the one hand, it provides promising results in patient interactions; on the other hand, it supports the work of rehabilitators. It is worth noting that the main strength of the solution is the inference and suggestions proposed by the system during a session with the patient; taking into account the dynamic development of artificial intelligence systems and the fact that each subsequent session provides new data, we can count on the beneficial impact of the presented system on both the progress in the patient's rehabilitation and also the quality and effectiveness of the rehabilitator's work.

Light-Adapted Optoelectronic-Memristive Device for the Artificial Visual System.

With the development of artificial intelligence systems, it is necessary to develop optoelectronic devices with photoresponse and storage capacity to simulate human visual perception systems. The key to an artificial visual perception system is to integrate components with both sensing and storage capabilities of illumination information. Although module integration components have made useful progress, they still face challenges such as multispectral response and high energy consumption. Here, we developed a light-adapted optoelectronic-memristive device integrated by an organic photodetector and ferroelectric-based memristor to simulate human visual perception. ITO/P3HT:PC71BM/Au as the light sensor unit shows a high on/off ratio (Iph/Id) reaching ∼5 × 104 at 0 V. The memristor unit, consisting of ITO/CBI@P(VDF-TrFE)/Cu, has a RON/ROFF ratio window of ∼106 under 0.05 V read voltage and ultralow power consumption of ∼1 pW. Moreover, the artificial visual perception unit shows stable light-adapted memory windows under different wavelengths of irradiation light (400, 500, and 600 nm; they meet the spectral range of human visual recognition) and can clearly identify the target image ("T" shape) because of the apparent contrast, which results from the high ROFF/RON ratio values. These results provide a potential design strategy for the development of intelligent artificial vision systems.

Ethical approaches in designing autonomous and intelligent systems: a comprehensive survey towards responsible development

AbstractOver the past decade, significant progress in artificial intelligence (AI) has spurred the adoption of its algorithms, addressing previously daunting challenges. Alongside these remarkable strides, there has been a simultaneous increase in model complexity and reliance on opaque AI models, lacking transparency. In numerous scenarios, the systems themselves may necessitate making decisions entailing ethical dimensions. Consequently, it has become imperative to devise solutions to integrate ethical considerations into AI system development practices, facilitating broader utilization of AI systems across various domains. Research endeavors should explore innovative approaches to enhance ethical principles in AI systems, fostering greater transparency, accountability, and trustworthiness. Upholding fundamental individual rights, human dignity, autonomy, privacy, equality, and fairness, while mitigating potential harm, remains paramount. Considering ethical values and ensuring compliance with ethical requirements throughout the development lifecycle of autonomous and intelligent systems nurture trust and reliability in their utilization. Ethical considerations should be ingrained within organizational procedures guiding AI research activities, establishing robust frameworks that address ethical concerns and reflect the ethical implications of AI-based systems. This paper presents an overview of ethical approaches and processes aimed at integrating ethical considerations into AI system development practices. It underscores the significance of ethical frameworks in fostering ethical AI implementation and ensuring the ethical integrity of AI technologies.

Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.

To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC). Recent technical advances allow complete local excision of T2 CRC, traditionally treated with surgical resection. Yet, the widespread adoption of this approach is hampered by the inability to stratify the risk of LNM. Data from pT2 CRC patients undergoing surgical resection between April 2000 and May 2022 at one Japanese and one Italian center were analyzed. Primary goal was AI system development for accurate LNM prediction. Predictors encompassed seven variables: age, sex, tumor size and location, lympho-vascular invasion, histological differentiation, and carcinoembryonic antigen level. The tool's discriminating power was assessed via Area Under the Curve (AUC), sensitivity, and specificity. Out of 735 initial patients, 692 were eligible. Training and validation cohorts comprised of 492 and 200 patients, respectively. The AI model displayed an AUC of 0.75 in the combined validation dataset. Sensitivity for LNM prediction was 97.8% and specificity was 15.6%. The Positive and the Negative Predictive Value were 25.7% and 96% respectively. The False Negative (FN) rate was 2.2%, the False Positive was 84.4%. Our AI model, based on easily accessible clinical and pathological variables, moderately predicts LNM in T2 CRC. However, the risk of FN needs to be considered. The training of the model including more patients across Western and Eastern centers -differentiating between colon and rectal cancers- may improve its performance and accuracy.

Redefining the Skills Required on the Labour Market in the Context of the Development of Artificial Intelligence Systems. Case Study on Finnish Universities

The rise of AI technology across different sectors has drastically influenced jobs and employment overall. This paper's goal is to identify and redefine the fundamental skills that are needed in the job market in relation to the massive application of artificial intelligence. The methodology used includes a case study and document analysis. The case study is centred on the Finnish universities that have implemented a project to connect academic training and professional requirements in the era of artificial intelligence. The results of this paper suggest a radical shift from the usual and well-known labour-oriented competencies to a far more complex cognitive and interdisciplinary array of skills like complex problem-solving, emotional intelligence, and flexibility. Most importantly, the case study that this paper brings forward underscores that the educational process and innovation must be sustained in the job market because technology can never be stagnant and is continuously redrawing the boundaries of the job's responsibilities. In a nutshell, the competencies required in the labour market will have to be recast with the integration and development of artificial intelligence systems. Such developments need proactive actions from stakeholders, especially educational institutions, through which a proper channel is established for the smooth coexistence of the workforce in the future headed by artificial intelligence.

Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study

Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas. The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787. The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas]. We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making. Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.

Transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.

World Industrial Revolutions and the Development of Artificial Intelligence System

World Industrial Revolutions and the Development of Artificial Intelligence System

Towards trustworthy AI: An analysis of the relationship between explainability and trust in AI systems

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

DEVELOPMENT OF INTELLIGENT SYSTEMS FOR AUTOMATIC ASSESSMENT OF STUDENTS' ACADEMIC ACHIEVEMENTS

to analyse the development of artificial intelligence systems for automatic assessment of students' learning achievements. Methodology: to achieve this goal, the scientific methods of analysis and synthesis, content analysis, SWOT analysis, comparison, and typology were used. Results: it has been established that among the key advantages is a significant increase in the objectivity of the assessment of students' knowledge and skills. It is important to consider the acceleration of the process of checking the results, which saves time and effort for teachers. Another important advantage is the provision of real-time feedback during assessment. Scientific novelty: It has been established that one of the major problems is the possibility of bias and inequality in the educational system. Given that intelligent systems are based on certain algorithms, any bias or false information in the initial data can lead to biased results. Additional challenges include the excessive mechanisation of the assessment process, which does not always allow for the individual characteristics of each student, as well as ensuring appropriate protection of personal data. Conclusions: Intelligent student assessment systems are a powerful tool in countering corruption schemes in the educational system, especially in developing countries.

Expanded Brain CT Dataset for the Development of AI Systems for Intracranial Hemorrhage Detection and Classification

Intracranial hemorrhage (ICH) is a dangerous life-threatening condition leading to disability. Timely and high-quality diagnosis plays a huge role in the course and outcome of this disease. The gold standard in determining ICH is computed tomography. This method requires a prompt involvement of highly qualified personnel, which is not always possible, for example, in case of a staff shortage or increased workload. In such a situation, every minute counts, and time can be lost. The solution to this problem seems to be a set of diagnostic decisions, including the use of artificial intelligence, which will help to identify patients with ICH in a timely manner and provide prompt and quality medical care. However, the main obstacle to the development of artificial intelligence is a lack of high-quality datasets for training and testing. In this paper, we present a dataset including 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH, enriched with clinical and technical parameters, as well as the methodology of its generation utilizing natural language processing tools. The dataset is publicly available, which contributes to increased competition in the development of artificial intelligence systems and their advancement and quality improvement.

Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems

Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems

History of Technological Evolution in the Brazilian Judiciary System and the Application of Artificial Intelligence

Artificial Intelligence systems have been increasingly developing, demonstrating new forms of implementation and utilization, being employed in many sectors. In this regard, aiming to keep up with social development and enhance the development of procedural acts, the Brazilian Judiciary System has been seeking to implement new forms of technology over the years. Although gradually, an evolution can be observed in the sector, such as options that circumvent the use of physical documents and a departure from conducting all acts in person. This has led to the implementation and development of Artificial Intelligence systems within the judiciary, aiming to further improve the Brazilian judicial system. Therefore, this present work seeks to provide a historical overview of the advancements that have occurred in the Brazilian Judiciary System regarding its technological evolution, describing the initial modifications as well as the current applications of Artificial Intelligence. Additionally, it presents their objectives and capabilities. Thus, in conclusion, this paper demonstrates that the progress made over the years is immeasurable, and the application of Artificial Intelligence systems in the Brazilian Judiciary System is already becoming consolidated, with the potential for even greater growth in the coming years.

Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability

Artificial Intelligence (AI) is rapidly integrating into various aspects of our daily lives, influencing decision-making processes in areas such as targeted advertising and matchmaking algorithms. As AI systems become increasingly sophisticated, ensuring their transparency and explainability becomes crucial. Functional transparency is a fundamental aspect of algorithmic decision-making systems, allowing stakeholders to comprehend the inner workings of these systems and enabling them to evaluate their fairness and accuracy. However, achieving functional transparency poses significant challenges that need to be addressed. In this paper, we propose a design for user-centered compliant-by-design transparency in transparent systems. We emphasize that the development of transparent and explainable AI systems is a complex and multidisciplinary endeavor, necessitating collaboration among researchers from diverse fields such as computer science, artificial intelligence, ethics, law, and social science. By providing a comprehensive understanding of the challenges associated with transparency in AI systems and proposing a user-centered design framework, we aim to facilitate the development of AI systems that are accountable, trustworthy, and aligned with societal values.

Artificial intelligence in urban warfare: opportunities to enhance the protection of civilians?

As the hostilities in Ukrainian cities remind us once again, urban warfare persistently causes immense suffering and devastating consequences for civilians’ lives and livelihoods. At the same time, the events reveal the challenges urban warfare represents for militaries. The urban environment is one of the most complex environments within which to conduct military operations. To address the challenges this environment poses to militaries, several technologically advanced States are investing in the development of artificial intelligence to enhance a range of their military activities. The ways States have thus far prioritized the development of artificial intelligence systems, however, evidence that investments to improve militaries’ ability to mitigate civilian harm during urban warfare remain rather neglected. Hence, this article aims to demonstrate that despite the risks related to artificial intelligence applications, this technology has great potential for enhancing militaries’ ability to mitigate civilian harm further. But this requires governments to invest more in its development and use to that end. Recognizing artificial intelligence systems’ potential to reduce the military challenge of protecting civilians from harm during urban warfare is a pressing need, considering that conflicts increasingly occur in urban environments where the risks for civilians’ lives and their livelihoods grow exponentially.

DESIGNING ETHICAL ARTIFICIAL INTELLIGENCE (AI) SYSTEMS WITH MEANINGFUL YOUTH PARTICIPATION: IMPLICATIONS AND CONSIDERATIONS

While artificial intelligence (AI) enabled systems have shown impressive accuracy in detecting harmful content online, they are still not perfect and do not take into account the perspective of children in their design. The development of AI systems heavily relies on large datasets for training, and creating such datasets involves annotating vast amounts of data. Studies that involve children in dataset development also have their challenges, such as the possibility of re-traumatisation. Therefore, ethical considerations must be taken into account, such as obtaining informed consent, conducting design sessions with children and young people, and addressing implicit and explicit biases in AI filtering, profiling, and surveillance systems. It is crucial to involve children and young people in the design of AI systems that filter content to ensure ethical considerations are met. In this article we discuss the ethical concerns in AI development with children and young people, and also possible techniques that help mitigate such concerns.

Validation of an automated artificial intelligence system for 12‑lead ECG interpretation

BackgroundThe electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE. MethodsAn AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12‑lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. ResultsA total of 932,711 standard 12‑lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses. ConclusionsOur results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12‑lead ECG, highlighting its potential as a clinical tool for healthcare professionals.

From Artificial Intelligence and Bayesian Statistics to Neuroanatomy: Connections, Analogies, and Applications

This study examines the interaction between artificial intelligence (AI), Bayesian statistics, and various key brain structures, such as the hippocampus, amygdala, and thalamic nuclei. The goal is to explore how Bayesian inference can contribute to the development of AI systems that simulate and optimize essential aspects of human cognition, such as decision-making, attention, learning, and memory. By analyzing the functions of these structures within the framework of Bayesian statistics, possible avenues are identified for improving the adaptability and efficiency of AI systems in problem-solving and decision-making. Additionally, the relevance of Brodmann areas in the context of AI and Bayesian statistics is considered. The knowledge gained from this study can provide a solid foundation for the design of increasingly advanced and human-centric AI systems, facilitating more effective and understandable interaction between humans and AI technologies.

Semantic features of complex technosocial systems: On the taxonomy of artificial intelligence technological packages

Objectives. The aim ofthis work is to enhance the scientific and methodological apparatus of artificial intelligence (AI) sciences by enriching their conceptual framework. The current conceptual framework of AI sciences does not reflect the intricate nature of this technological and socioeconomic phenomenon as possessing the diverse range of capabilities and the interconnectedness that allows for the imitation of human cognitive functions and comparable results. The author of the article structures the concept of the technological package of AI, describing its system properties, connections and functional elements based on the various types of human cognitive and operational activities.Methods. The research is based on the concept (method) of technological packages—genetically and functionally connected sets of technologies with system properties.Results. For the first time in Russian and international practice, the basic (general) taxonomy of the AI technological package has been specified and structured. A taxonomy of the AI metatechnological package (a package of metatechnologies) has been proposed. General taxonomy can serve as a tool for improving strategies, methodological documents and state programs to define the development of AI systems at state or industry level.Conclusions. The suggested basic (general) taxonomy oftechnological package and taxonomy of metatechnologies package allows research to move away from the limited view of AI. It increases semantic and methodological clarity in relation to AI as a complex technosocial phenomenon and contributes to the harmonized integration of AI systems intо the sphere of socioeconomic activities of the state. It can thus serve as a foundation for further improvement of state economic and legal regulation of AI development.