From small brains to smart machines: translating Caenorhabditis elegans neural circuits into artificial intelligence

Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the issue of ensuring that AI systems operate ethically and produce results that align with ethical considerations is of great importance. In this work, we investigate the combination of several key parameters on the performance of artificial neural networks (ANNs) used for medical image analysis in the presence of data corruption or errors. For this purpose, we examined five different ANN architectures (AlexNet, LeNet 5, VGG16, ResNet-50, and Vision Transformers - ViT), and for each architecture, we checked its performance under varying combinations of training dataset sizes and percentages of images that are corrupted through mislabeling. The image mislabeling simulates deliberate or nondeliberate changes to the dataset, which may cause the AI system to produce unreliable results. We found that the five ANN architectures produce different results for the same task, both for cases with and without dataset modification, which implies that the selection of which ANN architecture to implement may have ethical aspects that need to be considered. We also found that label corruption resulted in a mixture of performance metrics tendencies, indicating that it is difficult to conclude whether label corruption has occurred. Our findings demonstrate the relation between ethics in AI and ANN architecture implementation and AI computational parameters used therefor, and raise awareness of the need to find appropriate ways to determine whether label corruption has occurred.

The article analyzes the influence of artificial intelligence on various spheres of social life: educational, cultural, economic, and others, from the standpoint of understanding the role of a person and setting their thinking under the influence of artificial intelligence systems. The influence of artificial intelligence on the spheres of human life raises the inevitable question of the transformation of the legal norms governing these areas. No matter how technologically advanced artificial intelligence systems are, there is always a risk of making an incorrect decision, which can be associated with causing damage to property, harm to life and health. An important and conceptual problematic areas of regulation is the development of a model of responsibility for causing harm using artificial intelligence and robotics systems. Approaches to identifying the culprit are considered in the article. Several situations stand out when deciding the issue of responsibility for causing harm when using artificial intelligence systems. Firstly, situations of deliberate distortion of program code at the stage of creating or training an artificial intelligence system are possible. In such a situation, of course, the responsibility falls on the person involved in the creation or training. Given that, as a rule, several persons are involved in such a process, difficulties may arise with the identification of a specific guilty subject, similarly to the situation when a violent crime is committed by a group of persons and a fatal blow is inflicted by one of the members of the group. Secondly, situations are possible when harm is caused due to the illegal seizure of control of the artificial intelligence system. In such a situation, responsibility also lies with the attacker - a physical person. Thirdly, there may be situations when, in the process of self-learning, artificial intelligence systems come to certain conclusions that do not depend on the actions of developers, and, as a result, harm is caused. Disputes about the responsibility of such artificial intelligence just arise in the event of the autonomy of its decision, that is, regardless of human actions. It is concluded that at this stage our society is not ready for the recognition of artificial intelligence as an independent subject capable of bearing responsibility, especially when it comes to the criminal legal sphere. To resolve the issue of the possibility of recognition (or non-recognition) of artificial intelligence as an independent entity of responsibility, it is necessary to proceed from the general provisions of the theory of legal responsibility, correlating its constituent elements with the characteristics of artificial intelligence systems, which requires independent comprehension and research. The authors declare no conflicts of interests.

This paper investigates the training performances of multilayer artificial neural network (ANN) architectures for the implementation of chaotic systems. The designed ANN models can be employed for pattern recognition and prediction of dynamic chaotic systems, in order to simulate and analyze brain activities captured by Electroencephalogram (EEG). Previous research shows that EEG signals demonstrate chaotic features. Chaotic systems can be represented by a set of mathematical equations, which can be used to generate the target outputs for training ANN. In this research, the Henon map is selected as an example for ANN-based chaotic system design. The optimization of ANN architecture is important for improving the performance of hardware implementation. ANN architectures with up to 3 hidden layers combined with different number of hidden neurons are compared by measuring the training performance using the mean square errors (MSE). The ANN training are carried out using three training algorithms: Levenberg-Marquardt, Bayesian Regulation and Scaled Conjugated Gradient. Nonlinear autoregressive (NAR) model is used for ANN architectures design. The training results demonstrate that the training performance can not be improved simply by increasing the complexity of the ANN architecture in terms of the number of hidden layers and hidden neurons. It is therefore necessary to optimize the ANN architecture on a case-by-case basis in order to improve the efficiency of the ANN implementation for specified applications.

Artificial intelligence to complement rather than replace radiologists in breast screening

Audit agencies such as the United States, the United Kingdom, the Netherlands, and Brazil conduct audits by artificial intelligence systems to ensure the effectiveness of the return of illegal supply and demand, illegal and unfair measures of duties, and the appropriateness of audits. In Korea, the Framework Act on Artificial Intelligence was enacted in 2025, and Article 20 of the General Act on Public Admiaiatration on Administration stipulates the automatic disposition of artificial intelligence for administrative disposition of binding acts. The Audit Office Act and the Public Audit Act require the establishment and operation of an audit-related information system, but it cannot be concluded as an artificial intelligence information system because it does not define the concept of the information system. Since there are no prestigious regulations on the scope and target of audit activities by audit agencies, standards, confidentiality, registration and certification of audit artificial intelligence systems, etc., securing predictability, transparency, and legality of artificial intelligence audits is not guaranteed. In addition, the concept of artificial intelligence audit should include not only the audit of the artificial intelligence system, but also all audit activities using the artificial intelligence system by auditors or audit agencies. In particular, it is necessary to establish a third-party auditor system as well as internal auditors and external auditors for audit by artificial intelligence systems. The U.S. California bill on artificial intelligence audit was proposed to the California Legislature in February 2025. It establishes an artificial intelligence system on the Internet of state audit and operation agencies to database information on audits, prepare procedures for registration, certification, and verification of artificial intelligence auditors and artificial intelligence systems, as well as disclosure and confidentiality of audit information, the subject of information to be provided to artificial intelligence systems, the explainability of artificial intelligence audits, and the obligation to store audit information for 10 years, but administrative or public institutions do not mandate the introduction of artificial intelligence systems. In this paper, after establishing the concept of artificial intelligence audit, the possibility of expanding artificial intelligence of audit in the intelligent information society and measures to prevent corruption are reviewed. In addition, since the category of artificial intelligence audit is unclear in Korea's artificial intelligence law and audit-related laws, the scope of artificial intelligence audit is clearly identified, and implications for Korea are sought by reviewing and analyzing artificial intelligence audit cases in the United States and the Netherlands in the public administration area. In addition, the legal task of artificial intelligence audit is ⅰ) the enactment of the Artificial Intelligence Audit Act, ⅱ) the acceptability of automatic decision-making of artificial intelligence audit, ⅲ) clarification of the standards and scope of artificial intelligence audit, ⅳ) legal task for the use of artificial intelligence to prevent fraud and illegal supply and demand, and ⅴ) the issue of strengthening new technology capabilities for future auditors and audit institutions are reviewed in five taps and suggested ways to improve them.

Audit agencies such as the United States, the United Kingdom, the Netherlands, and Brazil conduct audits by artificial intelligence systems to ensure the effectiveness of the return of illegal supply and demand, illegal and unfair measures of duties, and the appropriateness of audits. In Korea, the Framework Act on Artificial Intelligence was enacted in 2025, and Article 20 of the General Act on Public Admiaiatration on Administration stipulates the automatic disposition of artificial intelligence for administrative disposition of binding acts. The Audit Office Act and the Public Audit Act require the establishment and operation of an audit-related information system, but it cannot be concluded as an artificial intelligence information system because it does not define the concept of the information system. Since there are no prestigious regulations on the scope and target of audit activities by audit agencies, standards, confidentiality, registration and certification of audit artificial intelligence systems, etc., securing predictability, transparency, and legality of artificial intelligence audits is not guaranteed. In addition, the concept of artificial intelligence audit should include not only the audit of the artificial intelligence system, but also all audit activities using the artificial intelligence system by auditors or audit agencies. In particular, it is necessary to establish a third-party auditor system as well as internal auditors and external auditors for audit by artificial intelligence systems. The U.S. California bill on artificial intelligence audit was proposed to the California Legislature in February 2025. It establishes an artificial intelligence system on the Internet of state audit and operation agencies to database information on audits, prepare procedures for registration, certification, and verification of artificial intelligence auditors and artificial intelligence systems, as well as disclosure and confidentiality of audit information, the subject of information to be provided to artificial intelligence systems, the explainability of artificial intelligence audits, and the obligation to store audit information for 10 years, but administrative or public institutions do not mandate the introduction of artificial intelligence systems. In this paper, after establishing the concept of artificial intelligence audit, the possibility of expanding artificial intelligence of audit in the intelligent information society and measures to prevent corruption are reviewed. In addition, since the category of artificial intelligence audit is unclear in Korea's artificial intelligence law and audit-related laws, the scope of artificial intelligence audit is clearly identified, and implications for Korea are sought by reviewing and analyzing artificial intelligence audit cases in the United States and the Netherlands in the public administration area. In addition, the legal task of artificial intelligence audit is ⅰ) the enactment of the Artificial Intelligence Audit Act, ⅱ) the acceptability of automatic decision-making of artificial intelligence audit, ⅲ) clarification of the standards and scope of artificial intelligence audit, ⅳ) legal task for the use of artificial intelligence to prevent fraud and illegal supply and demand, and ⅴ) the issue of strengthening new technology capabilities for future auditors and audit institutions are reviewed in five taps and suggested ways to improve them.

‘Equality and Privacy by Design’: Ensuring Artificial Intelligence (AI) Is Properly Trained & Fed: A New Model of AI Data Transparency & Certification As Safe Harbor Procedures

As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdisciplinary collaboration between HCI and AI. The recommendations in this article provide a starting point for designing such systems. In essence, XAI presents a significant opportunity to improve the transparency of AI systems, but it requires careful design and implementation to be effective.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

The article examines the legal aspects of ensuring the patient’s right to informed voluntary consent in the provision of psychiatric care using artificial intelligence (AI) systems. Overall, the use of AI opens new possibilities for the diagnosis and treatment of mental disorders, offering significant potential to enhance the effectiveness of psychiatric care. However, the application of these technologies introduces various risks for patients, particularly concerning the protection of autonomy, the transparency of AI algorithms, and the security of personal data. Patients with mental disorders represent a particularly vulnerable group requiring additional legal guarantees in decision-making regarding treatment, especially when innovative technologies are involved. Based on an analysis of existing technologies, the authors identify a number of risks associated with the use of AI systems in psychiatric care, including: 1) violations of personal data confidentiality; 2) risks associated with decisions made by AI systems; 3) potential discrimination based on gender, race, religion, or other characteristics; 4) misuse in medical practice through the use of AI; 5) risks arising from malfunctions in AI systems; 6) other potential hazards. To mitigate these risks, the article considers legal regulatory measures, including the introduction of European legislation such as the AI Act, certification implementation, and the establishment of effective mechanisms for informed voluntary consent to AI use in psychiatry, given the high risks posed by this technology. The authors note that Ukrainian legislation currently lacks adequate mechanisms for obtaining informed consent in the use of AI for psychiatric care. The article proposes improvements to Ukrainian regulatory acts through the development of a separate consent form for the use of AI systems in psychiatric assessment or treatment, which would help to avoid the legal risks inherent in AI systems. Such a consent form would include detailed information for the patient about the specific AI systems to be used, their nature, purpose, and estimated duration of use. It would also inform the patient that the data collected and processed by the AI system would be protected according to data protection legislation, and it would include a verbal explanation of risks by the physician, as well as the options for choosing alternative treatment methods based on the doctor’s recommendations. The conclusions emphasize the importance of advancing national legislation to align with the AI Development Concept and international certification standards. This will ensure the protection of patients’ rights and foster the effective integration of AI in the field of psychiatric care.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Is the future of medical diagnosis in computer algorithms?

Purpose Grounded in social cognitive theory, this study aims to develop a research framework centered on artificial intelligence (AI) self-efficacy to investigate the factors influencing health-care workers’ usage intention of AI systems. Design/methodology/approach This research used an online questionnaire with 210 valid questionnaires collected from hospital workers serving a regional teaching hospital in central Taiwan. A partial least squares-structural equation modeling (PLS-SEM) and artificial neural network (ANN) approach were used to analyze the users’ responses. First, PLS-SEM was used to validate the model and hypotheses; in the following stage, the ANN approach was used to rank the importance of each influencing factor on the usage intention of AI systems. Findings This study identified the associations among environment (others’ encouragement, usage and support), person (users’ AI self-efficacy, personal outcome expectations, positive affect and users’ AI anxiety) and behavior (usage intention). Moreover, the other model using ANN validated the results of the SEM analyses that affect, outcome expectations and AI anxiety were essential influences on AI system usage intention. Practical implications In the health-care field, AI anxiety is mainly manifested in a multifaceted unease, including doctors’ and patients’ concerns about the accuracy and safety of AI in diagnosis and surgery, health-care workers’ fears that AI may replace their work or weaken their professional authority and technological dependency and privacy issues that deepen trust and security concerns. Therefore, when introducing AI systems into administrative processes, hospital administrators can develop measures that prioritize improving hospital workers’ affect and outcome expectations for using AI systems and alleviating workers’ AI anxiety to increase workers’ intention to use AI systems. Originality/value The model design and definition provide a concrete and fundamental framework for the research and application of the AI system intention to use; adopting the PLS-SEM-ANN methodology improves the accuracy and interpretability of the model assumption validation. This method combines PLS-SEM, used for linear hypothesis testing, with ANN, which excels at capturing nonlinear relationships, providing a robust framework that enhances flexibility in complex analyses, theoretical validation and predictive accuracy. The study results provide an empirical basis for hospital administrators to improve the intention of hospital staff to use AI systems.

The question of expediency and the principal possibility of machine imitation of human intellect from the point of view of evaluating the perspectives of various directions of development of artificial intelligence systems is discussed. It is shown that even beyond this practical aspect, the solution to the question about the principal possibility of creating a machine equivalent of the human mind is of great importance for understanding the nature of human thinking, consciousness and mental in general. It is noted that the accumulated experience of creating various systems of artificial intelligence, as well as the currently available results of studies of human intelligence and human consciousness in philosophy and psychology allow us to give a preliminary assessment of the prospects of creating an algorithmic artificial system, equal in its capabilities to human intelligence. The analysis of the drawbacks revealed in the use of artificial intelligence systems by mass users and in scientific research is carried out. The key disadvantages of artificial intelligence systems are the inability to independently set goals, the inability to form a consolidated «opinion» when working with divergent data, the inability to objectively evaluate the results obtained and generate revolutionary new ideas and approaches. The disadvantages of the «second level» are the insufficiency of information accumulated by mankind for further training of artificial intelligence systems, the resulting training of models on the content partially synthesized by artificial intelligence systems themselves, which leads to «forgetting» part of the information obtained during training and increasing the cases of issuing unreliable information. This, in turn, makes it necessary to check the reliability of each answer given by the artificial intelligence system whenever critical information is processed, which, against the background of the plausibility of the data given by artificial intelligence systems and a comfortable form of their presentation, requires the user to have well-developed critical thinking. It is concluded that the main advantage of artificial intelligence systems is that they can significantly increase the efficiency of information retrieval and primary processing, especially when dealing with large data sets. The importance of the ethical component in artificial intelligence and the creation of a regulatory framework that introduces responsibility for the harm that may be caused by the use of artificial intelligence systems is substantiated, especially for multimodal artificial intelligence systems. The conclusion is made that the risks associated with the use of multimodal artificial intelligence systems consistently increase in the case of realization in them of such functions of human consciousness as will, emotions and following moral principles.

<p><b>Artificial intelligence systems have become proficient at linking environmental features to targets to describe simple patterns in data. However, these systems can struggle with many real-world problems that entail hierarchical patterns within patterns, for example, in recognizing object ontologies where one object is made-up of other objects. Although it is possible to capture such complex structures by utilizing state-of-the-art deep networks, the knowledge is often stored in layers that do not take advantage of the potential benefits provided by reusing patterns within a layer of the system.</b></p> <p>Biological nervous systems can learn knowledge from simple and small-scale problems and then apply it to resolve more complex and large-scale problems in similar and related domains. However, rudimentary attempts to apply this transfer learning in artificial intelligence systems have struggled. This may be due to the homogeneous nature of their knowledge representation. The current understanding of the learning mechanisms in the brains of human and non-human animals can be used as inspiration to improve learning in artificial agents. Research into lateral asymmetry of the brain shows that it enables modular learning at different levels of abstraction that facilitate transfer between tasks.</p> <p>The proposed thesis is that an artificial intelligence system that enables lateralization and modular learning at different levels of abstraction has the ability to solve complex hierarchical problems that a similar homogeneous system can not. The comprehensive goal of this thesis is to accomplish lateralized learning, inspired by the principles of biological intelligence, in artificial intelligence systems. The objectives are to show that lateralization and modular learning assist the novel systems to encapsulate the underlying knowledge patterns in the form of building blocks of knowledge. These building blocks of knowledge are to be tested on analyzable Boolean tasks as well as practical computer vision and navigation tasks. Academic contributions are related to the novel methods of the linking, transfer, and sharing of learned knowledge which are based on the analogous strategies of the brain.</p> <p>This thesis proposes a general framework for lateralized artificial intelligence systems. The novel lateralized framework spans key aspects of knowledge perception, knowledge representation and utilization, and patterns of connectivity. It determines the essential functionality, critical methods, and associated parameters that are required to be incorporated into an artificial intelligence system to behave as a lateralized artificial intelligence system.</p> <p>This thesis creates a novel evolutionary machine learning system, by adapting the lateralized framework, to obtain a proof-of-concept of the lateralized approach. Considering the same problem at different levels of abstraction enables the novel system to reframe a complex problem as a simple problem and efficiently resolve it. The results on analyzable Boolean tasks show that the problems that contain a natural hierarchy of patterns are solved to a scale that exceeds previous work (i.e. 18-bit hierarchical multiplexer problem), and reusing learned general patterns as constituents for future problems advances transfer learning (e.g. n-bit parity problem effectively becomes a sequence of 2-bit parity problems). </p> <p>This thesis creates a novel lateralized artificial intelligence system, by adapting the lateralized framework, that shows robustness in a real-world domain that includes uncertainty, noise, and irrelevant and redundant data. The results of image classification tasks show that the lateralized system efficiently learns hierarchical distributions of knowledge, demonstrating performance that is similar to (or better than) other state-of-the-art deep systems as it reasons using multiple representations. Crucially, the novel system outperformed all the state-of-the-art deep models for the classification (binary classes) of normal and adversarial images by 0.43%-2.56% and 2.15%-25.84%, respectively. This thesis creates another novel multi-class lateralized system for computer vision problems to show that the lateralized approach can be scaled and not limited to learning classifier systems.</p> <p>Both the Boolean and computer vision problems are single step problems in the spatial domain. However, most biological tasks, which exhibit heterogeneity, are temporal in nature. This thesis creates a novel frame-of-reference based artificial intelligence system, by adapting the lateralized framework, to address perceptual aliasing in multi-step decision making tasks. Considering aliased states at a constituent level enables the novel system to place them appropriately in holistic level policies. Consequently, the novel system transforms a non-Markov environment into a deterministic environment and efficiently resolves it. Experimental results show that the novel system effectively solves complex aliasing patterns in non-Markov environments that have been challenging to artificial agents. For example, the novel system utilizes only 6.5, 3.71, and 3.22 steps to resolve Maze10, Littman57, and Woods102, respectively.</p> <p>A final contribution of this work is to obtain evidence of the benefits/costs of lateralization from artificial intelligence in order to inform cognitive neuroscience. Given that lateralization is ubiquitous in brains, evolutionary benefits can be assumed, at least in some domains. But that does not mean those benefits extend to all domains. The cognitive neuroscience research community has been struggling to determine the trade-off between the benefits and costs of lateralization. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Lateralization has been associated with both poor and good performance. This thesis demonstrates the value of viable artificial systems for testing the costs and benefits of lateralization in biological systems.</p>