Artificial intelligence and geopolitics

Study question Do artificial intelligence (AI) models used to assess embryo viability (based on pregnancy outcomes) also correlate with known embryo quality measures such as Gardner score? Summary answer An AI for embryo viability assessment also correlated with Gardner score, further substantiating the use of AI for assessment and selection of good quality embryos. What is known already The Gardner score consists of three separate components of embryo morphology that are graded individually, then combined to give a final score describing Day 5 embryo (blastocyst) quality. Evidence suggests the Gardner score has some correlation with clinical pregnancy. We hypothesized that an AI model trained to evaluate likelihood of clinical pregnancy based on fetal heartbeat (in clinical use globally) would also correlate with components of the Gardner score itself. We also compared the ability of the AI and Gardner score to predict pregnancy outcomes. Study design, size, duration This study involved analysis of a prospectively collected dataset of single static Day 5 embryo images with associated Gardner scores and AI viability scores. The dataset comprised time-lapse images of 1,485 embryos (EmbryoScope) from 638 patients treated at a single in vitro fertilization (IVF) clinic between November 2019 and December 2020. The AI model was not trained on data from this clinic. Participants/materials, setting, methods Average patient age was 35.4 years. Embryologists manually graded each embryo using the Gardner method, then subsequently used the AI to obtain a score between 0 (predicted non-viable, unlikely to lead to a pregnancy) and 10 (predicted viable, likely to lead to a pregnancy). Correlation between the AI viability score and Gardner score was then assessed. Main results and the role of chance The average AI score was significantly correlated with the three components of the Gardner score: expansion grade, inner cell mass (ICM) grade, and trophectoderm grade. Average AI score generally increased with advancing blastocyst developmental stage. Blastocysts with expansion grades of ≥ 3 are generally considered suitable for transfer. This study showed that embryos with expansion grade 3 had lower AI scores than those with grades 4-6, consistent with a reduced pregnancy rate. AI correlation with trophectoderm grade was more significant than with ICM grade, consistent with studies demonstrating that trophectoderm grade is more important than ICM in determining clinical pregnancy likelihood. The AI predicted Gardner scores of ≥ 2BB with an accuracy of 71.7% (sensitivity 75.1%, specificity 45.9%), and an AUC of 0.68. However, when used to predict pregnancy outcome, the AI performed 27.9% better than the Gardner score (accuracies of 49.8% and 39.0% respectively). Even though the AI was highly correlated with the Gardner score, the improved efficacy for predicting pregnancy suggests that a) the AI provides an advantage in standardization of scoring over the manual and subjective Gardner method, and b) the AI is likely identifying and evaluating morphological features of embryo quality that are not captured by the Gardner method. Limitations, reasons for caution The Gardner score is not a linear score, creating challenges with setting a suitable threshold relating to the prediction of pregnancy. The 2BB treshold was chosen based on literature (Munné et al 2019) and verified by experienced embryologists. This correlative study may also require additional confirmatory studies on independent datasets. Wider implications of the findings The correlation between AI scores and known features of embryo quality (Gardner score) substantiates the use of the AI for embryo assessment. The AI score provides further insight into components of the Gardner score, and may detect morphological features related to clinical pregnancy beyond those evaluated by the Gardner method. Trial registration number Not applicable

This research gives consideration to the legal status of artificial intelligence technology. Artificial intelligence as a future technology is actively expanding its capabilities at the present stage of development of society. In this regard, the concept of ‘artificial intelligence’ and the application of the rule of right in resolving issues of legal responsibility for the operation of artificial intelligence technologies require definition. The main purpose of this study is to define the concept of ‘artificial intelligence’ and determine whether artificial intelligence technologies are the object or subject of right. The article provides the analysis of possible approaches to the disclosure of the concept of ‘artificial intelligence’ as a legal category and its relationship with the concepts of ‘robot’ and ‘cyberphysical system’. The issues of legal responsibility for the operation of artificial intelligence are revealed. For the purposes hereof, the methods of collecting and studying the singularities; generalizations; the methods of scientific abstraction; the methods of cognition of consistent patterns, as well as the method of objectivity, concreteness, pluralism and a whole range of other methods were used. The study has concluded that the artificial intelligence technology is an autonomous self-organizing computer-software or cyberphysical system with the ability and capability to think, learn, make decisions independently, perceive and model surrounding images and symbols, relationships, processes and implement its own decisions. The following general properties of artificial intelligence technologies have been defined: autonomy; the ability to perceive the conditions (situation), make and implement own decisions; the ability to adapt own behavior, to learn, to communicate with other artificial intelligence, to consider, accumulate and reproduce experience (including human experience). Within the present historical period, artificial intelligence technology should be considered as the object of right. The legal responsibility for the operation of artificial intelligence lies with the operator or another person who sets the parameters of its operation and controls its behavior. The creator (manufacturer) of artificial intelligence is also recognized as a responsible person. This conclusion makes it possible to enter the category of artificial intelligence in the legal field and determine the persons responsible for its poor-quality operation.

This paper traces the evolutionary trajectory of artificial intelligence from narrow artificial intelligence (ANI) through artificial general intelligence (AGI) to potential artificial superintelligence (ASI), critically examining the technological singularity as a transformative inflection point in technological and human development. The analysis covers key technical milestones required for this progression, including advances in machine learning architectures, knowledge representation, and cognitive architectures. Drawing from recent developments in neuroscience and deep learning, the paper proposes a framework for understanding the qualitative differences between these three stages of artificial intelligence development. The paper addresses critical questions about intelligence, consciousness and the challenges of maintaining human agency in a post-singularity world. Novel perspectives on safety mechanisms and technical guidelines necessary for managing the transition between these developmental stages are presented. The analysis acknowledges the uncertainty surrounding the path to AGI and ASI, highlighting the complex and unpredictable nature of advanced artificial intelligence development. The paper concludes by exploring potential trajectories of recursive self-improvement in artificial systems and their theoretical implications for the future of machine cognition.

Digital musical synthesizer with artificial intelligence: a new field of musical possibilities is the direction that people all over the planet are currently concerned about: from professional musicians, musical sound engineers, composers and performers of music in various genres; from music teachers in various educational institutions to students and their parents; from producers to listeners -music lovers.This article attempts to identify the most significant aspects of the use of artificial intelligence in music and related phenomena from the point of view of the authors.

Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high‐quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.

Understanding Artificial Intelligence (AI) is on its way to becoming a key competence in the coming years due to its rapid advancements and growing societal impact. Learning about different paradigms in AI, particularly in machine learning, is crucial to enabling students to critically engage with and shape an AI-driven world. The study investigates how students of three grade levels (5th, 8th, and 11th) understand AI concepts, addressing the challenge of widespread misconceptions and the limited presence of AI in school curricula. A deductive qualitative analysis was used to analyze the students' preconceptions. The results indicate that younger students typically exhibit minimal or anthropomorphic views, reflecting limited exposure to basic AI principles. Older students show somewhat more advanced, yet still partial, understandings, with misconceptions persisting across all groups. The findings underscore the importance of integrating AI literacy into school curricula and aligning instruction with students' developmental stages. For younger learners, hands-on activities can introduce basic AI functionality while dispelling human-like attributions. Older students benefit from exploring ethical, technical, and societal dimensions of AI. This research highlights the need for age-appropriate AI education to foster informed, responsible users and creators of AI systems.

Background: Artificial intelligence (AI) is increasingly applied in medicine, including plastic and reconstructive surgery, to enhance diagnostic accuracy, surgical planning, outcome evaluation, and efficiency. However, integration into clinical practice remains limited. This systematic review assessed the current peer-reviewed clinical applications of AI across all plastic surgery subspecialties. Methods: Following PRISMA guidelines, we searched Medline, Embase, Cochrane, and PubMed for English-language studies (2015–2025) on AI in plastic/reconstructive surgery. Inclusion was limited to peer-reviewed clinical studies involving patients or patient data. Data on subspecialty, AI use-case, performance, and stage of development were extracted. Study quality was appraised with a modified MINORS tool. Results: The initial search yielded 2,153 records; 24 studies met all inclusion criteria. All major subspecialties were represented, especially aesthetic, breast and craniofacial. AI was applied across all subdisciplines, most commonly in aesthetic/cosmetic and craniofacial surgery. Key applications included image-based diagnostics, predictive analytics for surgical outcomes, augmented reality for surgical planning, and chatbot tools for patient education. Many algorithms achieved high accuracy or expert-level performance in research settings. However, the research was largely early-stage: most studies were retrospective and focused on model development (preclinical) with only one study demonstrating clinical implementation as of 2022. Quality appraisal showed that while nearly all studies had clearly stated aims and appropriate endpoints, only ~20% were prospective and only ~10–15% compared AI performance to current standards or clinicians. Overfitting was a concern, with just ~40% reporting use of validation techniques. Overall, included studies showed moderate methodological quality. Conclusions: AI applications in plastic surgery expanded substantially over the last decade, showing promise in improving diagnostic accuracy, surgical planning, and patient counseling. Nevertheless, most studies remain preliminary, with limited clinical translation to date. Stronger study designs – including prospective trials, external validation, and direct comparisons to standard care – are needed to establish the real-world efficacy of AI. Future research and clearer regulatory guidance are essential to safely integrate AI into routine plastic surgical practice.

Artificial intelligence has recently become one of the hot topics in scientific and professional circles of various fields. Current technological processes in the world have significantly increased the use of digital technologies in practically all fields. Over the past century, several researchers and mathematicians have been developing the idea that computing machines can not only perform typical technical tasks but also learn to think and perform individual tasks accordingly, like humans. This idea has developed over time, and nowadays, the main topic among scientists, researchers, and practitioners worldwide is artificial intelligence and the tasks it can perform. Artificial intelligence is already actively used in various fields of public activity and many professions, and there is still talk that many professions can be replaced by artificial intelligence in the future. In this regard, using artificial intelligence in jurisprudence is particularly important and controversial. Based on the relevance of the mentioned issue, this article is dedicated to the use of artificial intelligence in the field of criminal proceedings and investigations. The article discusses the origin and development of artificial intelligence and its primary role in criminal proceedings and investigations. In the work, special attention is paid to the experience of different countries. The paper also analyzes court decisions of precedent importance in this regard. The paper also contains the main challenges of using artificial intelligence in criminal justice and investigation.

BackgroundColorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.ObjectiveThis study uses bibliometrics to analyze research in AI in CRC, summarize the field’s history and current status of research, and predict future research directions.MethodWe searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.ResultThis study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field’s most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.ConclusionAI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Purpose of review Artificial intelligence (AI) is increasingly prevalent in the clinical workplace, a trend that is likely to continue with the amount of attention and resources these technologies receive. This review of 22 articles from the last 18 months takes stock of not only the prospects but also the challenges for clinicians resulting from AI integration. Recent findings While the technology matures rapidly, insights into organizational processes and user readiness and involvement in AI development, implementation, and deployment lag behind. AI impact assessments often focus narrowly on task efficiency, overlooking the derived effect of additional workload elsewhere. Additionally, the issue of the distribution of responsibility between humans and AIs poses a fundamental ethical, legal, and political challenge. Research acknowledges the need to consider healthcare professionals’ diverse roles and sociocultural backgrounds to avoid AI exacerbating existing inequalities among the clinical workforce and, ultimately, the patients cared for. Summary Decision-makers should involve users throughout the entire AI life cycle, from the early stages of AI development to continuous postdeployment impact assessment on workload. More research is needed on AI's cost-effectiveness, integration into clinical practice, and the role of diversity-aware facilitation in realizing its potential.

This thesis takes the expression intellectual property (IP) with Chinese characteristics as a departure point to reflect on the interplay between IP, industrial and innovation policies in contemporary China. Inspired by legal anthropology and political economy, it aims at highlighting what is concealed and what is elicited by the expression. In around 40 years, China went from a nearly non-existent to a very stringent IP system. It is the largest applicant of patents in the world, and actively engages at the World Trade Organization (WTO) and the World Intellectual Property Organization (WIPO). Persistent counterfeiting, access to medicines, artificial intelligence, and reduced freedom to operate are main issues at stake. In this context, the thesis compares the Made in China and the Made in China 2025, which represent the shift from an economic model based on relatively cheap manufacturing with little IP protection to the pursuits of Chinas technological dominance with hightechnology and strong IP protection. It provides a historical overview and assesses the recent amendments in the period 2019-2021, which are based on foreign pressure by the US-China trade war and the interests of domestic firms in IP protection. It also conducts an analysis of some of the main contemporary aspects of the Chinese IP system, including the expansion in IP applications, the use of national security, the creation of specialized courts, policies to create a culture of IP in schools and universities, and ostensive anti-counterfeiting policies. Subsequently, the research deals with how the IP with Chinese characteristics reverberates internationally. To that aim, it conducts an analysis of Chinas stances at the World Trade Organization (WTO) and the World Intellectual Property Organization (WIPO), where the country tends to adopt intermediary positions. This section is partly based on a series of interviews and an ethnographic experience as participant observer in Geneva (2018-2021). It also develops Chinas IP stances in the Regional Comprehensive Economic Partnership (RCEP) and its Belt and Road Initiative (BRI), as examples of its increased role in regional and bilateral IP. China conducts procedural harmonization and cooperation-based activities but does not push its own Chinese legal standards. Therefore, it concludes that there is no Chinese standard in IP, but its role as norm-maker will invariably continue to grow. This may offer opportunities for Latin America. In the following chapter, the research addresses the politics of pharmaceutical patents and Covid-19 vaccines. It provides an analysis of IP and access to medicines in China, and its adoption of multiple TRIPS-Plus norms, which largely reduces the policy space of the country to ensure affordable access to medicines. The non-IP regulatory scope of tools available to the Chinese State can limit the detrimental impacts in China but may not be enough. The chapter then assesses the pharmaceutical sector in the country, noting how Chinese companies aim at becoming global big pharma, which this is in line with Chinas aspirations to achieve technological dominance. At the World Health Organization (WHO), China has committed to treating vaccines as global public goods. At the same time, it has incentivized patenting of all technologies via fasttrack policies, and heavily invested and coordinated the Covid-19 R&D research. China has been the main provider of Covid-19 vaccines in the global south but uses it to advance its geopolitical interests. The Sinovac-Butantan partnership highlights some interactions and a transnational publicprivate regulation between China and Brazil. The chapter presents the debates on the WTO TRIPS waiver proposal and the mostly cautious, background role of China despite its key position in the access to Covid-19 vaccines. It concludes with a parallel between the idea of vaccine diplomacy and IP nationalism are correlated issues. The next chapter argues that IP is part of a nation-building process and surrounded by a specter of modernity. In contemporary China, this is associated to a forward-looking and futuristic ideal that positively values the high-tech. For this reason, the figure of the IP pirate is a public enemy to be combatted since it represents the backwards and the illicit. However, categories of copy and authenticity are not pre-existing, but constructed something which Tianducheng, a Chinese copy of Paris with its own aura, elucidates. The concluding remarks argue that China offers a lesson for countries to use their policy space to conduct IP and innovation policies differently from the expectations of Western countries. However, it does not present a techno-diverse, critical alternative: instead, it reinforces existing structures. At the very end, drawing a comparison with Brazil, the thesis concludes with the need to envision alternative intellectual properties, which should not be based on exclusionary concepts of nationalisms and the private, but rather on inclusionary ideas of global and the public.

This paper uses bibliometric method and knowledge graph visualization technology to analyze the 237 papers of CNKI core journals from 2006 to 2021, from the key words, number of papers, authors, publishing institutions and high-impact journals. Statistical analysis, explaining the research status and research hotspots of artificial intelligence life cycle, and expounding the shortcomings and trends of research. The research on artificial intelligence technology is divided into exploratory stage and development stage according to the annual publication volume. From 2006 to 2015, the research hotspots mainly focus on "artificial intelligence" and " neural network ". From 2016 to 2021, the research hotspots mainly focus on three aspects: "artificial intelligence", "artificial intelligence technology" and "deep learning", and the research is gradually deepened, with a total of 223 journal articles. The research of artificial intelligence technology is in the development stage, and various fields are actively studying artificial intelligence technology, but the existing research focuses on the application level, the deep learning theory is not perfect, the basic technology and basic theory are ignored, and there is a lack of solutions to the problem of privacy leakage. Future research should pay more attention to basic technology and innovative research. There may be broader research space for research from two aspects: "integration and breakthrough of deep learning theory" and "machine learning evolution towards distributed privacy protection".

The article is devoted to analysis of the stages of development and current directions of research and practical application of artificial intelligence (AI) in the field of healthcare and medicine based on scientific publications in PubMed/MEDLINE, Scopus, Web of Science, Embase, eLibrary and CyberLeninka. The dynamics of scientific publications on AI in healthcare and medicine is shown, and an analysis of the growth of investments in software development based on AI in recent years is provided. AI can achieve comparable accuracy in the diagnosis of diseases in comparison with human decisions. However, future research should focus on comparing the clinical results of diagnosis and treatment performed by doctors who make decisions based on AI with the results of clinical work by doctors who do not use AI. The importance of training specialists able to combine knowledge in the field of medicine with the skills of using AI is emphasized.

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

As artificial intelligence (AI) becomes progressively more engaged with society, its shift from technical tool to participating in society raises questions about AI personhood. Drawing upon developmental psychology and systems theory, a mediating structure for AI proto‐personhood is defined analogous to an early stage of human development. The proposed AI bridges technical, psychological, and theological perspectives on near‐future AI and is structured by its hardware, software, computational, and sociotechnical systems through which it experiences its world as embodied (even for putatively disembodied AI). Further social and moral construction can occur building upon a simple “self” for AI synthesized from symbolic and statistical approaches to AI.