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Objective: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cytology specimens., Methods: The study cohort comprised 105 consecutive cytology specimens with metastatic breast carcinoma. ER IHC WSIs were seamlessly integrated into the Visiopharm platform from the Image Management System (IMS) during our routine digital workflow, and an AI algorithm was employed for analysis. ER AI scores were compared with pathologists' manual consensus scores. Optimization steps were implemented and evaluated to reduce discordance., Results: The overall concordance between pathologists' scores and AI scores was excellent (99/105, 94.3%). Six cases exhibited discordant results, including two false-negative (FN) cases due to abundant histiocytes incorrectly counted as negatively stained tumour cells by AI, two FN cases owing to weak staining, and two false-positive (FP) cases where pigmented macrophages were erroneously counted as positively stained tumour cells by AI. The Pearson correlation coefficient of ER-positive percentages between pathologists' and AI scores was 0.8483. Optimization steps, such as lowering the cut-off threshold and additional training using higher input magnification, significantly improved accuracy., Conclusions: The automated ER AI algorithm demonstrated excellent concordance with pathologists' assessments and accurately differentiated ER-positive from ER-negative metastatic breast carcinoma cytology cases. However, precision in identifying tumour cells in cytology specimens requires further enhancement., (© 2024 John Wiley & Sons Ltd.)

The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology., (© 2024 The Authors. Histopathology published by John Wiley & Sons Ltd.)

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician-AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice., (© 2024. The Author(s).)

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed., (© 2023 John Wiley & Sons Ltd.)

The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications., (© 2023. Springer-Verlag GmbH Germany, part of Springer Nature.)

This study explores the transformative potential of Generative AI (GenAI) and ChatBots in educational interaction, communication, and the broader implications of human-GenAI collaboration. By examining the related literature through data mining and analytical methods, the paper identifies three main research themes: the revolutionary role of GenAI-powered ChatBots in educational interactions, their capability to enrich social learning, and their dual role as both support and assistance within educational settings. This research further highlights the impact of human-GenAI interaction in education from social, psychological, and cultural perspectives, focusing on social presence as a fundamental component of the teaching and learning process. It discusses the integration of GenAI and ChatBots into education and considers whether this marks the dawn of an algorithmic renaissance that elevates educational experiences or an apocalypse that threatens the very essence of human learning and interaction.

Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing principles of improved decision tree algorithm, and the information gain of the algorithm determines the inheritance of information. The process of modern e-learning system is based on personalized teaching and humanized intelligent interaction. This paper theoretically analyzes the improvement performance of the existing e-learning system in teaching quality evaluation and shows a good classification effect. This model provides reference materials for the expansion of education and teaching and provides a feasible practical model for personalized teaching in online schools. The authors provide good educational conditions and environment for students and cultivate all-around talents for the society.

Firstly, this paper analyzes the role of AI in the reading management of English language and literature, establishes the implicit knowledge base of neural network, designs the auxiliary reading system for learning English language and literature, and optimizes the English language and literature management model of AI. The experimental results show that its reading efficiency is increased by 0.48%, and the performance of the credibility model is improved by 0.53% compared with the original system, which greatly optimizes the running time of the system. To some extent, it helps users to manage their time in English language and literature reading, and greatly improves users' reading efficiency and quality. Based on this advantage of AI algorithm, this paper introduces that the algorithm optimizes the reading management model and the training process of neural grid, and constructs a model of English language and literature assisted reading system based on AI. The system can better meet the needs of users in English language and literature reading management.

The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing innovative talent cultivation capability. First, by CIPP model (Context, Input, Process and Product Evaluation), an assessment indicator system is built, consisting of 89 indicators in 21 categories. Then, based on indicator characteristics, this paper uses public data statistics, database retrieving, student survey, teacher survey, support personnel and expert investigation, to collect indicator values. After this, by a powerful machine learning algorithm, gradient Boosting regression tree, a capability assessment model is established. And based on collected data, established model is compared with several regression models in innovative talent cultivation capability assessing. Results confirm the performance superiority of our solution.

Purpose: This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy. Design/methodology/approach: Data for this study include student interviews (called "talk backs"), journal reflections and final technology presentations. Findings: Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also "rebuild" and "reimagine" the algorithmic world. Originality/value: While critical race media literacy draws upon critical race theory in education - a theorization of race, and a critique of white supremacy and multiculturalism in schools - critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% (SD = 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days (SD = 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity (M = 42%, SD = 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology.

Simulation is a powerful approach that plays a significant role in science and technology. Computational models that simulate learner interactions and data hold great promise for educational technology as well. Amongst others, simulated learners can be used for teacher training, for generating and evaluating hypotheses on human learning, for developing adaptive learning algorithms, for building virtual worlds in which students can practice collaboration skills with simulated pals, and for testing learning environments. This paper provides the first systematic literature review on simulated learners in the broad area of artificial intelligence in education and related fields, focusing on the decade 2010-19. We analyze the trends regarding the use of simulated learners in educational technology within this decade, the purposes for which simulated learners are being used, and how the validity of the simulated learners is assessed. We find that simulated learner models tend to represent only narrow aspects of student learning. And, surprisingly, we also find that almost half of the studies using simulated learners do not provide "any" evidence that their modeling addresses the most fundamental question in simulation design -- is the model valid? This poses a threat to the reliability of results that are based on these models. Based on our findings, we propose that future research should focus on developing more complete simulated learner models. To validate these models, we suggest a standard and universal criterion, which is based on the lasting idea of Turing's Test. We discuss the properties of this test and its potential to move the field of simulated learners forward.

Matchmaking has a great position in the rational allocation of resources in several fields, ranging from market operation to people's daily lives. Matchmakers have evolved through artificial intelligence technologies and are being introduced in numerous aspects of industry, research, and academia in solving decision issues, research innovation design, and building robust and efficient networks. The goal of this report is to describe the collaborative platforms and matchmaking algorithms for research and education, as well as the establishment and optimization of consortia., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

Background: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging., Methods: We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes., Results: Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations., Conclusions: This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects., (© 2022. The Author(s).)

Technology Enabled Learning (TEL) has a major impact on the learning adaptability of the learners. During the COVID-19 pandemic, there has been a drastic change in the learning methodology. The adaptability of learners from the various domains, levels and age has been a significant component of research in context to education. In this paper, the authors have proposed a machine learning and explainable AI based solution to identify critical learning parameters for students' adaptability level in online education. In this research the authors have employed various explainable AI (XAI) algorithms namely Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), FEature iMportance based eXplanable AI algorithm (FAMeX) for identifying the critical learning parameters to decide the adaptability level of a student. To test the efficacy of the solution, a dataset of students of several education levels of Bangladesh, collected from both online and offline surveys has been used. The results revealed are quite interesting, and counter intuitive.

The continual evolution of employment opportunities in the present industrial era has raised the need for career-long expert advice. Similar to other fields, thankfully technology has come to our rescue in the area of career guidance also. This paper presents a systematic review of Expert Systems (ES) developed for career guidance, course selection and evaluation of students in the past ten years. The popular research databases Google Scholar and Science Direct were used for obtaining the relevant research papers through broad keywords. The keywords were refined to identify the articles related to rule-based, case-based and fuzzy logic-based ES used for career guidance. A total of twenty-five peer-reviewed relevant articles with full-text available online was selected for the final study. In order to avoid duplicity, technical reports and unreferenced literature were excluded. The review identifies the relatively high weight given by the researchers to rule-based systems owing to their simplicity and broad applicability. However, the relative merits and demerits of rule-based, case-based and fuzzy logic-based ES are highly dependent on the field of application. Nevertheless, ES find wide applications in the area of career guidance and have the potential to enhance the career guidance accessibility of the most remote students.

Recent negotiations of 'data' in schools place focus on student assessment and NAPLAN. However, with the rise in artificial intelligence (AI) underpinning educational technology, there is a need to shift focus towards the value of teachers' digital data. By doing so, the broader debate surrounding the implications of these technologies and rights within the classroom as a workplace becomes more apparent to practitioners and educational researchers. Drawing on the Australian Human Rights Commission's "Human Rights and Technology final report," this conceptual paper focusses on teachers' rights alongside emerging technologies that use or provide predictive analytics or artificial intelligence, also called 'personalisation'. The lens of Postdigital positionality guides the discussion. Three potential consequences are presented as provocations: (1) What might happen if emerging technology uses teachers' digital data that represent current societal inequality? (2) What might happen if insights provided by such technology are inaccurate, insufficient, or unrepresentative of our teachers? (3) What might happen if the design of the AI system itself is discriminatory? This conceptual paper argues for increased discourse about technologies that use or provide predictive analytics complemented by considering potential consequences associated with algorithmic bias.

Purpose: Artificial intelligence (AI) chatbots, such as ChatGPT and GPT-4, developed by OpenAI, have the potential to revolutionize education. This study explores the potential benefits and challenges of using ChatGPT in education (or "educative AI"). Design/Approach/Methods: This paper proposes a theoretical framework called "IDEE" for educative AI such as using ChatGPT and other generative AI in education, which includes identifying the desired outcomes, determining the appropriate level of automation, ensuring ethical considerations, and evaluating effectiveness. Findings: The benefits of using ChatGPT in education or more generally, educative AI, include a more personalized and efficient learning experience for students as well as easier and faster feedback for teachers. However, challenges such as the untested effectiveness of the technology, limitations in the quality of data, and ethical and safety concerns must also be considered. Originality/Value: This study explored the opportunities and challenges of using ChatGPT in education within the proposed theoretical framework.

This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was designed to be adapted to any field of learning. The most recent application of AI-VT was in the field of computer science specifically in the context of the fundamentals of algorithmic learning. AI-VT can and will also be useful in other disciplines. Developed in Python with the Keras API and the Tensorflow framework, this artificial intelligence-based tool encompasses a supervised learning environment, multi-label text classification techniques and deep neural networks. This paper presents and compares the performance levels of the different models tested on two different data sets in the context of computer programming and algorithms.

A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI.

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of hidden layers. In this paper, we study the relationship between both models in terms of model structures and model learning. For this purpose, we also compare the predictive performances of both models, with two practical examples.

Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students' learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students' performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students' academic performance. The results revealed that the dashboard significantly enhanced students' learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students' performance and assisted them in self-regulation

This paper reviews algorithmic or artificial intelligence (AI) bias in education technology, especially through the lenses of speculative fiction, speculative and liberatory design. It discusses the causes of the bias and reviews literature on various ways that algorithmic/AI bias manifests in education and in communities that are underrepresented in EdTech software development. While other recent work has responded to mainstream or private sector technology development, this review looks elsewhere where practitioners, artists, and activists engage underrepresented communities in brainstorming processes to identify and solve tough challenges. Their creative work includes films, toolkits, applications, prototypes and other physical artifacts, and other future-facing ideas that can provide guideposts for private sector development. Acknowledging the gaps in what has been studied, this paper proposes a different approach that includes speculative and liberatory design thinking, which can help developers better understand the educational and personal contexts of underrepresented groups. Early efforts to advocate for fairness and equity in AI and EdTech by groups such as the Algorithmic Justice League, the EdTech Equity Project, and EdSAFE AI Alliance is also explored.

Mathematics education is like many scientific disciplines witnessing an increase in scientific output. Examining and reviewing every paper in an area in detail are time-consuming, making comprehensive reviews a challenging task. Unsupervised machine learning algorithms like topic models have become increasingly popular in recent years. Their ability to summarize large amounts of unstructured text into coherent themes or topics allows researchers in any field to keep an overview of state of the art by creating automated literature reviews. In this article, we apply topic modeling in the context of mathematics education and showcase the use of this data science approach for creating literature reviews by training a model of all papers (n = 336) that have been presented in Thematic Working Groups related to technology in the first eleven Congresses of the European Society for Research in Mathematics Education (CERME). We guide the reader through the stepwise process of training a model and give recommendations for best practices and decisions that are decisive for the success of such an approach to a literature review. We find that research in this period revolved around 25 distinct topics that can be grouped into four clusters: digital tools, teachers and their resources, technology experimentation, and a diverse cluster with a strong focus on student activity. Finally, a temporal analysis of these topics reveals a correlation between technology trends and research focus, allowing for reflection on future research in the field.

In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn't delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts.

There is a broad impetus across policy and institutional domains to expand public engagement and involvement with emerging technology research and innovation. Yet innovative theory, methods, and practices to critically explore algorithmic system controversies and democratic possibilities are still in nascent form. In this paper, we bring together thinking about public pedagogy, infrastructuring, and democratic design to introduce a novel "pedagogic encounters toolkit," where pedagogic encounters are defined as: opportunities for learning together about the emergence, relationality, and uncertainty of algorithmic system controversies. A framework with accompanying practices and examples is outlined for researchers, policymakers, and practitioners to creatively and critically explore these pedagogic encounters within specific contexts. We conclude with implications for future investigations attentive to making democratic designs in a datafied society. In doing so, this paper informs theoretical and methodological innovation to the study of algorithmic system controversies and democratising technology.

This paper presents an application of the Kalman filter in signal processing in instrumentation systems when the conditions of the environment generate a large amount of interference for the acquisition of signals from measurement systems. The unwanted interferences make important use of the instrumentation system resources and do not represent useful information under any aspect. A simulation is presented using the Matlab tool, which remarkably facilitates the information processing so that the corresponding actions are taken according to the information obtained, taking advantage of the current resources offered by the embedded systems and the required measurements are obtained with enough accuracy.

Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification algorithm. In designing this tool, we aim to overcome the use of economic, social, and cultural context indices as main factors for the prediction of a learning gap occurrence. Indeed, we want to include a suitable representation of students' learning in the model, by exploiting the data collected through the INVALSI tests. We resort to a knowledge-based approach to address this issue and specifically, we try to understand what knowledge is introduced into the model through the representation of learning. In this sense, our proposal allows a students' learning encoding, which is transferable to different students' cohort. Furthermore, the encoding methods may be applied to other large-scale assessments test. Hence, we aim to contribute to a debate on knowledge representation in AI tool for education.

The role of knowledge graph encompasses the representation, organization, retrieval, reasoning, and application of knowledge, providing a rich and robust cognitive foundation for artificial intelligence systems and applications. When we learn new things, find out that some old information was wrong, see changes and progress happening, and adopt new technology standards, we need to update knowledge graphs. However, in some environments, the initial knowledge cannot be known. For example, we cannot have access to the full code of a software, even if we purchased it. In such circumstances, is there a way to update a knowledge graph without prior knowledge? In this paper, We are investigating whether there is a method for this situation within the framework of Dalal revision operators. We first proved that finding the optimal solution in this environment is a strongly NP-complete problem. For this purpose, we proposed two algorithms: Flaccid_search and Tight_search, which have different conditions, and we have proved that both algorithms can find the desired results., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang, Sun. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCT ischemia ) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCT ischemia among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion., Methods: AI-QCT ischemia was calculated by blinded analysts among patients from the retrospective coronary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on initial visual reading (diameter stenosis ≥50%) being referred for downstream 15 O-H 2 O-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCT ischemia . Absolute stress myocardial blood flow ≤2.3 ​ml/g/min in ≥2 adjacent segments was considered abnormal. The primary endpoint was death, myocardial infarction, or unstable angina pectoris. The median follow-up was 6.2 [IQR 4.4-8.3] years., Results: 662 of 768 (86%) patients had conclusive AI-QCT ischemia result. In patients with normal 15 O-H 2 O-PET perfusion, an abnormal AI-QCT ischemia result (n ​= ​147/331) vs. normal AI-QCT ischemia result (n ​= ​184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17-5.21, p ​= ​0.018). This did not pertain to patients with abnormal 15 O-H 2 O-PET perfusion (abnormal AI-QCT ischemia result (n ​= ​269/331) vs. normal AI-QCT ischemia result (n ​= ​62/331); adjusted HR 1.09, 95% CI 0.58-2.02, p ​= ​0.794) (p-interaction ​= ​0.039)., Conclusion: Among patients with obstructive CAD on coronary CTA referred for downstream 15 O-H 2 O-PET perfusion imaging, AI-QCT ischemia showed incremental prognostic value among patients with preserved perfusion by 15 O-H 2 O-PET imaging, but not among those with reduced perfusion., Competing Interests: Declaration of competing interest Dr. Bär received research grants to the institution from Medis Medical Imaging Systems, Bangerter-Rhyner Stiftung (Basel, Switzerland) and Abbott, outside the submitted work. Dr. Saraste received consultancy fees from Astra Zeneca and Pfizer, and speaker fees from Abbott, Astra Zeneca, Janssen, Novartis and Pfizer. Dr. Bax received speaker fees from Abbott. Drs Earls and Min are employees of and hold equity in Cleerly Inc. Dr. Knuuti received consultancy fees from GE Healthcare and Synektik and speaker fees from Bayer, Lundbeck, Boehringer-Ingelheim, Pfizer and Siemens, outside of the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques., Data: The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC)., Sources: PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques., Conclusions: AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies., Clinical Significance: AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report., Objective: This systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes., Methods: This systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate., Results: The project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024., Conclusions: Evaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance., Trial Registration: PROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747., International Registered Report Identifier (irrid): PRR1-10.2196/51614., (©Aditya U Kale, Henry David Jeffry Hogg, Russell Pearson, Ben Glocker, Su Golder, April Coombe, Justin Waring, Xiaoxuan Liu, David J Moore, Alastair K Denniston. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.06.2024.)

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making., (© 2024 IOP Publishing Ltd.)

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%., Competing Interests: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Objectives: We evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI)., Methods: We created methods for transforming raw data into a data framework for applying machine learning and natural language processing techniques for predicting falls and fractures. Strategies such as inclusion and reporting for multiple races, mixed data sources such as outpatient, inpatient, structured codes, and unstructured notes, and addressing missingness were applied to raw data to promote a reduction in bias. The raw data was carefully curated using validated definitions to create data variables such as age, race, gender, and healthcare utilization. For the formation of these variables, clinical, statistical, and data expertise were used. The research team included a variety of experts with diverse professional and demographic backgrounds to include diverse perspectives., Results: For the prediction of falls, information extracted from radiology reports was converted to a matrix for applying machine learning. The processing of the data resulted in an input of 5,377,673 reports to the machine learning algorithm, out of which 45,304 were flagged as positive and 5,332,369 as negative for falls. Processed data resulted in lower missingness and a better representation of race and diagnosis codes. For fractures, specialized algorithms extracted snippets of text around keywork "femoral" from dual x-ray absorptiometry (DXA) scans to identify femoral neck T-scores that are important for predicting fracture risk. The natural language processing algorithms yielded 98% accuracy and 2% error rate The methods to prepare data for input to artificial intelligence processes are reproducible and can be applied to other studies., Conclusion: The life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. When applying artificial intelligence methods, input data must be prepared optimally to reduce algorithmic bias, as biased output is harmful. Building AI-ready data frameworks that improve efficiency can contribute to transparency and reproducibility. The roadmap for the application of AI involves applying specialized techniques to input data, some of which are suggested here. This study highlights data curation aspects to be considered when preparing data for the application of artificial intelligence to reduce bias., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology according to fine-grained analytic grading rubrics that were developed for formative assessment. The experimental results show that our algorithms achieve a high-level of agreement with human experts, on par with previous work on automated assessment of scientific explanations in English, and that [approximately]500 examples are typically enough to build reliable scoring models. The main contribution is twofold. First, we present a conceptual framework for constructing analytic grading rubrics for scientific explanations, which are composed of dichotomous categories that generalize across items. These categories are designed to support automated guidance, but can also be used to provide a composite score. Second, we apply this approach in a new context -- Hebrew, which belongs to a group of languages known as Morphologically-Rich. In languages of this group, among them also Arabic and Turkish, each input token may consist of multiple lexical and functional units, making them particularly challenging for NLP. This is the first study on automatic assessment of scientific explanations (and more generally, of open-ended questions) in Hebrew, and among the firsts to do so in Morphologically-Rich Languages.

Recommender systems have become one of the main tools for personalized content filtering in the educational domain. Those who support teaching and learning activities, particularly, have gained increasing attention in the past years. This growing interest has motivated the emergence of new approaches and models in the field, in spite of it, there is a gap in literature about the current trends on how recommendations have been produced, how recommenders have been evaluated as well as what are the research limitations and opportunities for advancement in the field. In this regard, this paper reports the main findings of a systematic literature review covering these four dimensions. The study is based on the analysis of a set of primary studies (N = 16 out of 756, published from 2015 to 2020) included according to defined criteria. Results indicate that the hybrid approach has been the leading strategy for recommendation production. Concerning the purpose of the evaluation, the recommenders were evaluated mainly regarding the quality of accuracy and a reduced number of studies were found that investigated their pedagogical effectiveness. This evidence points to a potential research opportunity for the development of multidimensional evaluation frameworks that effectively support the verification of the impact of recommendations on the teaching and learning process. Also, we identify and discuss main limitations to clarify current difficulties that demand attention for future research.

Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potential inhibitors from Traditional Chinese Medicine Database. The potent top 10 compounds were selected as candidates by Dock Score. In order to further screen the candidates, we used numbers of machine learning regression models containing support vector machines, bagging, random forest and other regression algorithms, as well as deep neural network models to predict the activity of the candidates. In addition, as a traditional method, 2D QSAR (multiple linear regression) and 3D QSAR methods are also applied. The AI methods got a better performance than the traditional 2D QSAR method. Moreover, we also built a framework composed of deep neural networks and transformer to predict the binding affinity of candidates and DPP4. Artificial intelligence methods and QSAR models illustrated the compound, 2007_4105, was a potent inhibitor. The 2007_4105 compound was finally validated by molecular dynamics simulations. Combining all the models and algorithms constructed and the results, Hypecoum leptocarpum might be a potential and effective medicine herb for the treatment of DM., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)