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Objective: Computer vision was used to predict expert performance ratings from surgeon hand motions for tying and suturing tasks., Summary Background Data: Existing methods, including the objective structured assessment of technical skills (OSATS), have proven reliable, but do not readily discriminate at the task level. Computer vision may be used for evaluating distinct task performance throughout an operation., Methods: Open surgeries was videoed and surgeon hands were tracked without using sensors or markers. An expert panel of 3 attending surgeons rated tying and suturing video clips on continuous scales from 0 to 10 along 3 task measures adapted from the broader OSATS: motion economy, fluidity of motion, and tissue handling. Empirical models were developed to predict the expert consensus ratings based on the hand kinematic data records., Results: The predicted versus panel ratings for suturing had slopes from 0.73 to 1, and intercepts from 0.36 to 1.54 (Average R2 = 0.81). Predicted versus panel ratings for tying had slopes from 0.39 to 0.88, and intercepts from 0.79 to 4.36 (Average R2 = 0.57). The mean square error among predicted and expert ratings was consistently less than the mean squared difference among individual expert ratings and the eventual consensus ratings., Conclusions: The computer algorithm consistently predicted the panel ratings of individual tasks, and were more objective and reliable than individual assessment by surgical experts.

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods., (© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

The potential application of "expert systems" to the diagnosis and assessment of special-needs children is examined and existing prototype systems are reviewed. The future of this artificial intelligence technology is discussed in relation to emerging development tools designed for the creation of expert systems by the lay public. (Author)

Abstract: The development of human-level AI has been a core goal of the AI field since its inception, though at present it occupies only a fraction of the field''s efforts. To help understand the viability of this goal, this article presents an assessment of expert opinions regarding human-level AI research conducted at AGI-09, a conference for this AI specialty. We found that various experts strongly disagree with each other on certain matters, such as timing and ordering of key milestones. However, we did find that most experts expect human-level AI to be reached within upcoming decades, and all experts give at least some chance that some milestones will be reached within this time. Furthermore, a majority of experts surveyed favor an integrative approach to human-level AI rather than an approach centered on a particular technique. Finally, experts are skeptical about the impact of massive research funding, especially if it is concentrated in relatively few approaches. These results suggest that the possibility of achieving human-level AI in the near term should be given serious consideration. [Copyright &y& Elsevier]

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework 'epistemology-cum-ethics' to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment. [ABSTRACT FROM AUTHOR]

Introduction: Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department (ED). However, accurate assessment can be limited by user experience. Artificial intelligence (AI) has been proposed as a model to increase the accuracy of cardiac POCUS. However, there is limited evidence of the accuracy of AI in the clinical environment. The objective of this study was to determine the diagnostic accuracy of AI for identifying systolic and diastolic dysfunction compared with expert reviewers., Methods: This was a prospective, observational study of adult ED patients aged ≥45 years with risk factors for systolic and diastolic dysfunction. Ultrasound fellowship-trained physicians used an ultrasound machine with existing AI software and obtained parasternal long axis, parasternal short axis, and apical 4-chamber views of the heart. Systolic dysfunction was defined as ejection fraction (EF) < 50 % in at least two views using visual assessment or E-point septal separation >10 mm. Diastolic dysfunction was defined as an E:A < 0.8, or ≥ 2 of the following: septal e' < 7 cm/s or lateral e' < 10 cm/s, E:e' > 14, or left atrial volume > 34 mL/m 2 . AI was subsequently used to measure EF, E, A, septal e', and lateral e' velocities. The gold standard was systolic or diastolic dysfunction as assessed by two independent physicians with discordance resolved via consensus. We performed descriptive statistics (mean ± standard deviation) and calculated the sensitivity, specificity, positive likelihood ratio (LR+), and negative likelihood ratio (LR-) of the AI in determining systolic and diastolic dysfunction with 95 % confidence interval (CI). Subgroup analyses were performed by body mass index (BMI)., Results: We enrolled 220 patients, with 11 being excluded due to inadequate images, resulting in 209 patients being included in the study. Mean age was 60 ± 9 years, 51.7 % were women, and the mean BMI was 31 ± 8.1 mg/kg 2 . For assessing systolic dysfunction, AI was 85.7 % (95 %CI 57.2 % to 98.2 %) sensitive and 94.8 % (95 %CI 90.6 % to 97.5 %) specific with a LR+ of 16.4 (95 %CI 8.6 to 31.1) and LR- of 0.15 (95 % CI 0.04 to 0.54). For assessing diastolic dysfunction, AI was 91.9 % (95 %CI 85.6 % to 96.0 %) sensitive and 94.2 % (95 %CI 87.0 % to 98.1 %) specific with a LR+ of 15.8 (95 %CI 6.7 to 37.1) and a LR- of 0.09 (0.05 to 0.16). When analyzed by BMI, results were similar except for lower sensitivity in the BMI ≥ 30 vs BMI < 30 (100 % vs 80 %)., Conclusion: When compared with expert assessment, AI had high sensitivity and specificity for diagnosing both systolic and diastolic dysfunction., Competing Interests: Declaration of competing interest We have no conflicts of interest to declare and this manuscript has not been submitted elsewhere., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is a major breakthrough following failure of numerous previous trials. However, in the absence of therapeutic standards, diagnostic tools are a key challenge as functional parameters in GA are hard to provide. The majority of anatomical biomarkers are subclinical, necessitating advanced and sensitive image analyses. In contrast to fundus autofluorescence (FAF), optical coherence tomography (OCT) provides high-resolution visualization of neurosensory layers, including photoreceptors, and other features that are beyond the scope of human expert assessment. Artificial intelligence (AI)-based methodology strongly enhances identification and quantification of clinically relevant GA-related sub-phenotypes. Introduction of OCT-based biomarker analysis provides novel insight into the pathomechanisms of disease progression and therapeutic, moving beyond the limitations of conventional descriptive assessment. Accordingly, the Food and Drug Administration (FDA) has provided a paradigm-shift in recognizing ellipsoid zone (EZ) attenuation as a primary outcome measure in GA clinical trials. In this review, the transition from previous to future GA classification and management is described. With the advent of AI tools, diagnostic and therapeutic concepts have changed substantially in monitoring and screening of GA disease. Novel technology combined with pathophysiological knowledge and understanding of the therapeutic response to GA treatments, is currently opening the path for an automated, efficient and individualized patient care with great potential to improve access to timely treatment and reduce health disparities., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: The USA has the highest age-standardized prevalence of inflammatory bowel disease (IBD). Both genetic and environmental factors have been implicated in IBD flares and multiple strategies are centered around avoiding dietary triggers to maintain remission. Chat-based artificial intelligence (CB-AI) has shown great potential in enhancing patient education in medicine. We evaluate the role of CB-AI in patient education on dietary management of IBD., Methods: Six questions evaluating important concepts about the dietary management of IBD which then were posed to three CB-AI models - ChatGPT, BingChat, and YouChat three different times. All responses were graded for appropriateness and reliability by two physicians using dietary information from the Crohn's and Colitis Foundation. The responses were graded as reliably appropriate, reliably inappropriate, and unreliable. The expert assessment of the reviewing physicians was validated by the joint probability of agreement for two raters., Results: ChatGPT provided reliably appropriate responses to questions on dietary management of IBD more often than BingChat and YouChat. There were two questions that more than one CB-AI provided unreliable responses to. Each CB-AI provided examples within their responses, but the examples were not always appropriate. Whether the response was appropriate or not, CB-AIs mentioned consulting with an expert in the field. The inter-rater reliability was 88.9%., Discussion: CB-AIs have the potential to improve patient education and outcomes but studies evaluating their appropriateness for various health conditions are sparse. Our study showed that CB-AIs have the ability to provide appropriate answers to most questions regarding the dietary management of IBD., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Road crashes, although random events, frequently occur on roads. However, certain characteristic infrastructural locations require detailed analysis regarding the frequency of road crashes. This study examines the dynamics of road crashes at characteristic infrastructural locations in Serbia from 2018 to 2022, focusing on bridges, tunnels, railroad crossings, and road work zones. Using data on road crashes from official reports, the analysis includes trends in crash rates, fatalities, injuries, and material damage during the above-mentioned time frame. In addition to the data analysis, 22 experts from the fields of traffic engineering ranked the mentioned characteristic infrastructural locations in terms of road safety. The same questions were asked to six different artificial intelligence software programs. The findings reveal significant variations in crash rates across different infrastructures, with bridges and road work zones having the highest number of crashes. Expert assessment is in line with the analysis of the results, while artificial intelligence gives a completely opposite assessment. [ABSTRACT FROM AUTHOR]

Background: The demand for mental health (MH) services in the community continues to exceed supply. At the same time, technological developments make the use of artificial intelligence--empowered conversational agents (CAs) a real possibility to help fill this gap. Objective: The objective of this review was to identify existing empathic CA design architectures within the MH care sector and to assess their technical performance in detecting and responding to user emotions in terms of classification accuracy. In addition, the approaches used to evaluate empathic CAs within the MH care sector in terms of their acceptability to users were considered. Finally, this review aimed to identify limitations and future directions for empathic CAs in MH care. Methods: A systematic literature search was conducted across 6 academic databases to identify journal articles and conference proceedings using search terms covering 3 topics: "conversational agents," "mental health," and "empathy." Only studies discussing CA interventions for the MH care domain were eligible for this review, with both textual and vocal characteristics considered as possible data inputs. Quality was assessed using appropriate risk of bias and quality tools. Results: A total of 19 articles met all inclusion criteria. Most (12/19, 63%) of these empathic CA designs in MH care were machine learning (ML) based, with 26% (5/19) hybrid engines and 11% (2/19) rule-based systems. Among the ML-based CAs, 47% (9/19) used neural networks, with transformer-based architectures being well represented (7/19, 37%). The remaining 16% (3/19) of the ML models were unspecified. Technical assessments of these CAs focused on response accuracies and their ability to recognize, predict, and classify user emotions. While single-engine CAs demonstrated good accuracy, the hybrid engines achieved higher accuracy and provided more nuanced responses. Of the 19 studies, human evaluations were conducted in 16 (84%), with only 5 (26%) focusing directly on the CA's empathic features. All these papers used self-reports for measuring empathy, including single or multiple (scale) ratings or qualitative feedback from in-depth interviews. Only 1 (5%) paper included evaluations by both CA users and experts, adding more value to the process. Conclusions: The integration of CA design and its evaluation is crucial to produce empathic CAs. Future studies should focus on using a clear definition of empathy and standardized scales for empathy measurement, ideally including expert assessment. In addition, the diversity in measures used for technical assessment and evaluation poses a challenge for comparing CA performances, which future research should also address. However, CAs with good technical and empathic performance are already available to users of MH care services, showing promise for new applications, such as helpline services. [ABSTRACT FROM AUTHOR]

The article examines the impact of AI tools in extracurricular online dance classes on student learning outcomes. The approach to interactive dance learning within Choreography (which is the mandatory discipline) using innovative M-learning platforms and apps such as Moodle and STEEZY has been introduced. An educational experiment was conducted among 40 students of the Institute of Music and Choreography at Ningxia Pedagogical University. The dance accomplishments in the control and experimental groups were assessed using the Choreographic Creativity Rating Scale in three areas: physical skills, presentation, and creativity. The mean levels of dancers' choreographic skills, as assessed by experts and audience at the end of the educational experiment, were determined. Students' projects were presented in such directions as: Hip-hop, Open Style, K-pop, House, Breaking, Popping, Whacking, Krump, Jazz Funk. The assessment of 4 levels of dance choreography (level 1—below expectations, level 2—meets some expectations, level 3—fully meets expectations, and level 4—exceeds expectations) in the areas of physical skills, presentation, and creativity of dancers' skills made it possible to compare learning outcomes in the control group and the experimental group. The expert assessment of students' achievements suggested that additional online extracurricular activities contribute to better dance skills, effective development of dancers' physical (+1.6), presentation (+1.16) and creative (+1.01) skills. This article is intended for dance instructors developing effective courses using relevant digital tools. [ABSTRACT FROM AUTHOR]

Background: Image-enhanced endoscopy (IEE) has been used in the differentiation between neoplastic and non-neoplastic colorectal lesions through microvasculature analysis. This study aimed to evaluate the computer-aided diagnosis (CADx) mode of the CAD EYE system for the optical diagnosis of colorectal lesions and compare it with the performance of an expert, in addition to evaluating the computer-aided detection (CADe) mode in terms of polyp detection rate (PDR) and adenoma detection rate (ADR). Methods: A prospective study was conducted to evaluate the performance of CAD EYE using blue light imaging (BLI), dichotomizing lesions into hyperplastic and neoplastic, and of an expert based on the Japan Narrow-Band Imaging Expert Team (JNET) classification for the characterization of lesions. After white light imaging (WLI) diagnosis, magnification was used on all lesions, which were removed and examined histologically. Diagnostic criteria were evaluated, and PDR and ADR were calculated. Results: A total of 110 lesions (80 (72.7%) dysplastic lesions and 30 (27.3%) nondysplastic lesions) were evaluated in 52 patients, with a mean lesion size of 4.3 mm. Artificial intelligence (AI) analysis showed 81.8% accuracy, 76.3% sensitivity, 96.7% specificity, 98.5% positive predictive value (PPV), and 60.4% negative predictive value (NPV). The kappa value was 0.61, and the area under the receiver operating characteristic curve (AUC) was 0.87. Expert analysis showed 93.6% accuracy, 92.5% sensitivity, 96.7% specificity, 98.7% PPV, and 82.9% NPV. The kappa value was 0.85, and the AUC was 0.95. Overall, PDR was 67.6% and ADR was 45.9%. Conclusions: The CADx mode showed good accuracy in characterizing colorectal lesions, but the expert assessment was superior in almost all diagnostic criteria. PDR and ADR were high. [ABSTRACT FROM AUTHOR]

The labor market has been significantly impacted by the rapidly evolving global landscape, characterized by increased competition, globalization, demographic shifts, and digitization, leading to a demand for new skills and professions. The rapid pace of technological advancements, economic transformations, and changes in workplace practices necessitate that employees continuously adapt to new skill requirements. A quick assessment of these changes enables the identification of skill profiles and the activities of economic fields. This paper aims to utilize natural language processing technologies and data clustering methods to analyze the skill needs of Lithuanian employees, perform a cluster analysis of these skills, and create automated job profiles. The hypothesis that applying natural language processing and clustering in job profile analyzes can allow the real-time assessment of job skill demand changes was investigated. Over five hundred thousand job postings were analyzed to build job/position profiles for further decision-making. In the first stage, data were extracted from the job requirements of entire job advertisement texts. The regex procedure was found to have demonstrated the best results. Data vectorization for initial feature extraction was performed using BERT structure transformers (sentence transformers). Five dimensionality reduction methods were compared, with the UMAP technique producing the best results. The HDBSCAN method proved to be the most effective for clustering, though RCBMIDE also demonstrated a robust performance. Finally, job profile descriptions were generated using generative artificial intelligence based on the compiled job profile skills. Upon expert assessment of the created job profiles and their descriptions, it was concluded that the automated job advertisement analysis algorithm had shown successful results and could therefore be applied in practice. [ABSTRACT FROM AUTHOR]

Digitalization of all life spheres is the reality of modern world development. The global digitization creates a powerful information environment. Its navigation requires serious tools for structuring, systematizing and processing information. Digital processes, as the most progressive, are in constant development. Artificial Intelligence (AI) is gaining popularity. The latest product that has created a lot of discussion is chatGPT (Generative Pre-trained Transformer) from OpenAI, which is an artificial intelligence chatbot that demonstrates the ability of digital devices to perform the tasks inherent to intelligent beings. The paper shows some issues of using ChatGPT for making a new course of Law English, a curriculum, a syllabus at the tertiary level removing concerns related to its application and utilization. The purpose of the manuscript is to describe a real case of working out a professional English course for university students applying ChatGPT. The methods of analysis, synthesis, case study, expert assessment were used. The results are as follows: a new course for Law English training, a curriculum, a syllabus and a textbook using content created by ChatGPT were readied. The conclusion is that, nowadays, everything can be taught by a teacher partly with an AI. ChatGPT can be used for a wide variety of educational purposes, including providing information, generating necessary texts, tasks, tests, and answering many questions. Most of the mentioned ChatGPT services are important educational elements in language teaching and learning, they can be used for professional English course development. [ABSTRACT FROM AUTHOR]

The relevance of the research on changes in the requirements for the managers’ competence in tourism and hospitality aroused from the urgent companies’ need for new skills of employees, which are necessary not only to change the way of fulfilling current job, but also to support and to be accepted by the employees significant changes in the companies’ business priorities. The aim of the study is to assess the forecast and validity of changes in the requirements for the leaders’ competence in tourism and hospitality. To achieve this goal, the authors of the study used comparative analysis methods of regulatory legal documents, analytical, synoptical and other materials, methods of classification, systematization and generalization of the data obtained, methods of expert assessment, as well as methods and technologies of heuristic modeling and foresight. The authors of the article systematized the genesis of approaches to assessing the most significant competencies of managers in the future. Taking into account the analysis of the software skills transformations in the future professions and their demand in tourism and hospitality, the process of specialists’ interaction in the formation and promotion of hotel products in the context of digital transformation was modeled. Based on an expert survey, significant requirements for the managers’ knowledge in tourism and hospitality enterprises have been ranked, which can be used by hotel management in the training development and development programs for middle and senior managers. The materials of the article may be used by the owners and managers of tourism and hospitality companies and other organizations, educational organizations that train personnel in the direction of tourism and hospitality. [ABSTRACT FROM AUTHOR]

Attention focuses on minimizing the time to train a neural network so that it recognizes a specified set of a system's input parameters. In training the neural network, the error function must be minimized. This is important in expert assessment of solutions generated by a smart system for the design of manufacturing processes. In such a system, solutions are generated by the combined operation of numerous modules on the basis of logical rules. The system to be designed will generally be complex and may contain subsystems of different types that function according rules described by fuzzy logic and systems of precedents [1]. [ABSTRACT FROM AUTHOR]

The value of social studies learning for junior high school students in several schools still needs to be higher. One of the factors of low student scores is the need for more variety in learning methods used by teachers, low student motivation, and the use of learning media that could be more optimal. One way to do this is to develop a learning model with engaging learning media. This research aims to develop mobile understanding by integrating artificial intelligence at the evaluation stage. The methodology used in this research is the waterfall approach method with requirements analysis, design, implementation, testing, operation, and maintenance. The mobile learning prototype was tested on media experts and junior high school student respondents. The dimensions of expert assessment are reviewed from media appearance, quality and use of media, navigation, product strength, media interface, product attractiveness, and material delivery in the media. The dimensions of questions for students consist of 4 points: content, interface, feedback and assessment, and personalization. The research results show a mobile application’s results, including a brain gym menu, problem identification, materials, discussion forums, assignment results, and quizzes. The menus presented make it easier for students to learn. The questionnaire results from experts show that the highest average is 5.00, with a standard deviation of 0.00. The questionnaire results from student respondents had the highest average of 49.714, with a standard deviation of 0.169. The results of the scores obtained in the expert and student respondent questionnaires show that mobile learning is acceptable. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the International Journal of Semantic Computing, focusing on a special issue on Transdisciplinary Artificial Intelligence. The special issue includes two papers that were presented at the 4th IEEE International Conference on Transdisciplinary AI. The first paper discusses transparency in medical artificial intelligence systems, presenting four different methods to create transparency and suggesting criteria for their use. The second paper explores learning causal graphs in manufacturing domains using structural equation models, showing how expert knowledge can reduce the difference between learned cause-effect relationships and expert assessment. The guest editors for this special issue are Fabio Persia, Gary Glesener, and Julienne Greer. [Extracted from the article]

This study contributes to the literature on financial security by highlighting the relevance of the perceptions and resulting professional judgment of stakeholders. Assessing a company's financial security using only economic indicators--as suggested in the existing literature--would be inaccurate when undertaking a comprehensive study of financial security. Specifically, indices and indicators based on financial or managerial reporting calculated at any particular point in time, provide only a superficial understanding--and may even distort the overall picture. It has also been suggested that expert assessment is the most objective method, although it has disadvantages related to individual cognitive limitations. These limitations are not particular to artificial intelligence, which could assess an enterprise's financial security in a less biased way. However, by only imitating human behavior, it is not able to perceive and evaluate with intuition the dynamics of the company's development and holistically assess the financial condition--despite the possibility of learning and forecasting--because artificial intelligence is not able to think and predict, which, in an enterprise, is the most important skill of a manager. Therefore, the risk of developing artificial intelligence to assess a firm's financial security lies in a biased assessment of the enterprise's activities in general--and its financial security in particular. [ABSTRACT FROM AUTHOR]

This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study's protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures. [ABSTRACT FROM AUTHOR]

BACKGROUND: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities. METHODS: A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-contrast, T2, and FLAIR images were used as inputs into a 3D nnU-Net to predict 3 tumor subregions and resection cavities. We evaluated the performance of networks trained on pretreatment training cases (Pre-Rx network), posttreatment training cases (Post-Rx network), and both pre- and posttreatment cases (Combined networks). RESULTS: Segmentation performance was as good as or better than interrater reliability with median dice scores for main tumor subregions ranging from 0.82 to 0.94 and strong correlations between manually segmented and predicted total lesion volumes (0.94

Machine learning revolutionizes accuracy in diverse fields such as disease diagnosis, speech understanding, and sentiment analysis. However, its intricate architecture often obscures the decision-making process, creating a "black box" that hinders trust and limits its potential. This lack of transparency poses significant challenges, particularly in critical fields like the healthcare system. We present OntoXAI, a Semantic Web Rule Language (SWRL) based Explainable Artificial Intelligence (XAI) approach to address these challenges. OntoXAI leverages semantic technology and machine learning (ML) to enhance prediction accuracy and generate user-comprehensible natural language explanations in the context of dengue disease classification. OntoXAI can be summarized into three key aspects. (1) Creates a knowledge base that incorporates domain-specific knowledge related to the disease. This allows for the integration of expert knowledge into the classification process. (2) OntoXAI presents a diagnostic classification system that utilizes patient symptoms as input to classify the disease accurately. By leveraging ML algorithms, it achieves high accuracy in disease classification. (3) OntoXAI introduces SWRL and ontology to integrate explainable AI techniques with Open AI API, enabling a better understanding of the classification process. By combining the power of machine learning algorithms with the ability to provide transparent, human-understandable explanations through Open AI API, this approach offers several advantages in enhancing prediction accuracy, achieving levels of up to 96%. Overall, OntoXAI represents a significant advancement in the field of explainable AI, addressing the challenges of transparency and trust in machine learning systems, particularly in critical domains like healthcare. [ABSTRACT FROM AUTHOR]

Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally is dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective and rapid diagnoses. In this study, we aim to investigate the potential of AI diagnosis in OLP and evaluate its effectiveness in improving diagnostic accuracy and accelerating clinical decision making. A total of 128 confirmed OLP patients were included, and lesion images from various anatomical sites were collected. The diagnosis was performed using AI platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus, for AI directly identification and AI pre-training identification. After OLP feature training, the diagnostic accuracy of the AI platforms significantly improved, with the overall recognition rates of ChatGPT-4O, ChatGPT (Diagram-Date extension), and Claude Opus increasing from 59%, 68%, and 15% to 77%, 80%, and 50%, respectively. Additionally, the pre-training recognition rates for buccal mucosa reached 94%, 93%, and 56%, respectively. However, the AI platforms performed less effectively when recognizing lesions in less common sites and complex cases; for instance, the pre-training recognition rates for the gums were only 60%, 60%, and 20%, demonstrating significant limitations. The study highlights the strengths and limitations of different AI technologies and provides a reference for future AI applications in oral medicine. [ABSTRACT FROM AUTHOR]

Introduction: This study explores the implementation and evaluation of OwlMentor, an AI-powered learning environment designed to assist university students in comprehending scientific texts. OwlMentor was developed participatorily and then integrated into a course, with development and evaluation taking place over two semesters. It offers features like documentbased chats, automatic question generation, and quiz creation. Methods: We used the Technology Acceptance Model to assess system acceptance, examined learning outcomes, and explored the influence of general self-efficacy on system acceptance and OwlMentor use. Results: The results indicated complex relationships between perceived ease of use, perceived usefulness, and actual use, suggesting the need for more dynamic models of system acceptance. Although no direct correlation between OwlMentor use and learning gains was found, descriptive results indicated higher gains among users compared to non-users. Additionally, general selfefficacy was strongly related to perceived usefulness, intention to use, and actual use of the system. Discussion: These findings highlight the importance of aligning AI tools with students' needs and existing learning strategies to maximize their educational benefits. [ABSTRACT FROM AUTHOR]

Recent advancements in high-resolution imaging have significantly improved our understanding of microstructural changes in the skin and their relationship to the aging process. Line Field Confocal Optical Coherence Tomography (LC-OCT) provides detailed 3D insights into various skin layers, including the papillary dermis and its fibrous network. In this study, a deep learning model utilizing a 3D ResNet-18 network was trained to predict chronological age from LC-OCT images of 100 healthy Caucasian female volunteers, aged 20 to 70 years. The AI-based protocol focused on regions of interest delineated between the segmented dermal-epidermal junction and the superficial dermis, exploiting complex patterns within the collagen network for age prediction. The model achieved a mean absolute error of 4.2 years and exhibited a Pearson correlation coefficient of 0.937 with actual ages. Furthermore, there was a notable correlation (r = 0.87) between quantified clinical scoring, encompassing parameters such as firmness, elasticity, density, and wrinkle appearance, and the ages predicted by deep learning model. This strong correlation underscores how integrating emerging imaging technologies with deep learning can accelerate aging research and deepen our understanding of how alterations in skin microstructure are related to visible signs of aging. [ABSTRACT FROM AUTHOR]

This descriptive and cross-sectional study examines the visual perception of real and AI-generated photographs on Facebook, focusing on the highland context of Puno, Peru. Data were collected from 215 participants through a survey administered via Google Forms, and images were evaluated by 24 experts using a technical sheet from January to February 2024. The findings indicate positive public reactions, a preference for authentic images, and an ability to identify distortions in those generated by AI. Experts identified favourable technical aspects of the synthesised images. These findings indicate a complex interplay between photographic authenticity and AI technology in visual perception. [ABSTRACT FROM AUTHOR]

When faced with questions about artificial intelligence (AI), many surgeons respond with scepticism and rejection. However, in the realm of cardiac surgery, it is imperative that we embrace the potential of AI and adopt a proactive mindset. This systematic review utilizes PubMed® to explore the intersection of AI and cardiac surgery since 2017. AI has found applications in various aspects of cardiac surgery, including teaching aids, diagnostics, predictive outcomes, surgical assistance, and expertise. Nevertheless, challenges such as data computation errors, vulnerabilities to malware, and privacy concerns persist. While AI has limitations, its restricted capabilities without cognitive and emotional intelligence should lead us to cautiously and partially embrace this advancing technology to enhance patient care. [ABSTRACT FROM AUTHOR]

ChatGPT is a large language model trained on increasingly large datasets to perform diverse language-based tasks. It is capable of answering multiple-choice questions, such as those posed by diverse medical examinations. ChatGPT has been generating considerable attention in both academic and non-academic domains in recent months. In this study, we aimed to assess GPT's performance on anatomical multiple-choice questions retrieved from medical licensing examinations in Germany. Two different versions were compared. GPT-3.5 demonstrated moderate accuracy, correctly answering 60–64% of questions from the autumn 2022 and spring 2021 exams. In contrast, GPT-4.o showed significant improvement, achieving 93% accuracy on the autumn 2022 exam and 100% on the spring 2021 exam. When tested on 30 unique questions not available online, GPT-4.o maintained a 96% accuracy rate. Furthermore, GPT-4.o consistently outperformed medical students across six state exams, with a statistically significant mean score of 95.54% compared with the students' 72.15%. The study demonstrates that GPT-4.o outperforms both its predecessor, GPT-3.5, and a cohort of medical students, indicating its potential as a powerful tool in medical education and assessment. This improvement highlights the rapid evolution of LLMs and suggests that AI could play an increasingly important role in supporting and enhancing medical training, potentially offering supplementary resources for students and professionals. However, further research is needed to assess the limitations and practical applications of such AI systems in real-world medical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: To understand the processes behind the decline of pollinators, it is also essential to gain insight into their behaviour and identify the factors that drive it. This study focuses on the foraging behaviour of wild bumblebees in urban areas of Terceira, Azores, Portugal. We video-recorded buff-tailed bumblebees on flowering patches of Cretan bird's-foot trefoil, pink-headed knotweed, and red clover for five-minute intervals. We used computer vision-based deep learning models to detect bumblebees. Our results showed that flower cover was the only factor influencing the attractiveness of flower patches for flower-visiting bumblebees, while plant species had no effect. The time bumblebees spent on inflorescences was longer compared to their travelling time between inflorescences on the large-headed red clover than on the smaller-headed trefoil and knotweed. However, the overall time bumblebees spent on the inflorescences did not significantly differ among the plant species. Since our computer vision-based model achieved high accuracy in finding bumblebees on the target plant species, we confirmed that AI-based solutions can provide methods for studying pollinator behaviour and offer valuable insights to support conservation efforts. To understand the processes behind pollinator declines and for the conservation of pollination services, we need to understand fundamental drivers influencing pollinator behaviour. Here, we aimed to elucidate how wild bumblebees interact with three plant species and investigated their foraging behaviour with varying flower densities. We video-recorded Bombus terrestris in 60 × 60 cm quadrats of Lotus creticus, Persicaria capitata, and Trifolium pratense in urban areas of Terceira (Azores, Portugal). For the automated bumblebee detection and counting, we created deep learning-based computer vision models with custom datasets. We achieved high model accuracy of 0.88 for Lotus and Persicaria and 0.95 for Trifolium, indicating accurate bumblebee detection. In our study, flower cover was the only factor that influenced the attractiveness of flower patches, and plant species did not have an effect. We detected a significant positive effect of flower cover on the attractiveness of flower patches for flower-visiting bumblebees. The time spent per unit of inflorescence surface area was longer on the Trifolium than those on the Lotus and Persicaria. However, our result did not indicate significant differences in the time bumblebees spent on inflorescences among the three plant species. Here, we also justify computer vision-based analysis as a reliable tool for studying pollinator behavioural ecology. [ABSTRACT FROM AUTHOR]

Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE). Methods 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice. [ABSTRACT FROM AUTHOR]

Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material processing. Traditional methods often fall short in explaining the complex, real-world data observed in production. While ANN models, typically functioning as "black boxes", improve production efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical formulas, could enhance this efficiency further. This article proposes a general framework that leverages ANNs (i.e., Conditional Average Estimator—CAE) to explain predicted results alongside their graphical presentation, marking a significant improvement over previous approaches and those relying on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical representation of physical phenomena and their derivatives. Additionally, it analyzes the reliability and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic to probabilistic descriptions for better handling of real-world phenomena. The proposed approach addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better understand the phenomena and outcomes obtained by ANNs. [ABSTRACT FROM AUTHOR]

Early diagnosis of cancer allows for easy follow-up of patients' treatment processes. The utilization of microarray gene technology has become increasingly prevalent in the detection of cancer. However, the limited success of classical data mining methods can be attributed to the absence of a linear relationship among the data in microarray datasets. Artificial intelligence-based classification methods are employed to address classification challenges in datasets with a high number of attributes, due to the following reasons: This study presents a novel approach that combines adaptive particle swarm optimization (PSO) with the artificial bee colony (ABC) algorithm to effectively classify microarray datasets. Firstly, Chi2 and ANOVA F-test feature selection algorithms are applied to the microarray datasets to prevent the model from getting stuck in the local optimum. Thus, the most defining features in the dataset are selected, and the dimension is reduced. For classification success increases, we have selected optimal features with the proposed hybrid adaptive ABC + PSO, gray wolf algorithm (GWO), PSO, and ABC algorithms on the new dataset obtained. The last step is performed to classify using a support vector machine (SVM), an artificial neural network (ANN), and a nearest-neighbor (k-NN) algorithm. In the study, eight microarray datasets liver, lung, renal, pancreatic, prostate, breast, colorectal, and brain tumors were used. [ABSTRACT FROM AUTHOR]

Background: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. Methods: The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. Results: In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P =.012). The same trend was observed in Arabic, albeit without statistical significance (P =.082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P =.002). Conclusions: Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes. [ABSTRACT FROM AUTHOR]

Background: It appears that active video games (AVGs) and training apps that allow for physical activity (PA) in immersive virtual reality (VR) may be useful for sports, health-enhancing PA, and physical education (PE). Therefore, research is needed to identify their potential. Objective: The study aimed to evaluate the intensity and attractiveness of exercise during table tennis (TT) training in VR in arcade and simulation modes and to assess the potential for using such exercises in health-enhancing PA, sport, and PE. Methods: The research used the Racket Fury: Table Tennis VR. Exercise intensity during TT training in VR was evaluated by heart rate (HR) monitoring and rating of perceived exertion (RPE 6–20). The effectiveness of short-term TT training in VR was estimated based on the user's performance in playing against an opponent with artificial intelligence (AI), satisfaction with playing TT was measured using the Physical Activity Enjoyment Scale (PACES), and the potential usefulness of the tested app in PA, sport, and PE was assessed based on a questionnaire for participating PE teachers (30 participants). Results: PA intensity during TT training in VR expressed as a percentage of maximum heart rate (HRmax) was moderate but was significantly (p < 0.001; d=-0.830) higher in the easier arcade mode (69.50 ± 12.58%HRmax) than in the simulation mode (64.10 ± 9.67%HRmax). Despite the greater fatigue of respondents, user satisfaction was significantly higher in arcade mode. Users' performance when playing with AI was significantly better after 20 min of training in VR than before training. PE teachers recognize the great potential of the app. Conclusions: The application tested is characterized by a beneficial PA intensity, with its level depending on the game mode. Facilitating strokes during a game of virtual TT promotes increased intensity of exercise and increased enjoyment of the PA. Short-term TT training in VR improves playing skills in a virtual environment. PE teachers spoke highly of the app and recognized the potential for using VR technology in PA, sports, and school PE. [ABSTRACT FROM AUTHOR]

The continuous progression in scientific research and technological innovation, alongside the burgeoning field of artificial intelligence, has catalyzed the assimilation of intelligent human-machine integration systems into the surgical domain. Despite the significant enhancement in surgical precision and patient convenience afforded by the adoption of surgical robots, the practical application of this technology is not without its risks. This paper reviews the literature on HRA within surgical contexts, scrutinizing the typology of human errors, their underlying factors, utilization of HRA techniques, and the assessment of surgeons' skills, to champion the need for HRA methodologies that are specific to the healthcare sector. Our analysis of 37 articles suggests that HRA implementation in healthcare is still in its infancy. The predominant method, Observational Human Factors Reliability Analysis (OCHRA), has been in use since 1999, yet it is limited in its capacity to evaluate internal error patterns and falls short in methodological advancement compared to techniques utilized in industries like nuclear energy and aviation. Therefore, the healthcare community must earnestly reevaluate the suitability and sophistication of existing methods, ensuring they capture the intricate interplay of cognitive processes inherent in medical practice. This critical step is essential in the development of tailored methodologies that are contextually grounded and cognitively oriented. Furthermore, investigating the modulating factors of human error behavior in intelligent human-robot integration systems is set to become a focal point for future research into personnel reliability within this evolving domain. [ABSTRACT FROM AUTHOR]

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Background: Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black‐boxes due to the difficulty of understanding the decision‐making process used by the algorithm to obtain a specific result. Aims: For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis. Materials & Methods: Since the diagnostic task was a three‐class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme. Results: The accuracy of the three‐class model reaches an overall accuracy of 86.36%, and the precision per‐class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. Discussion: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system. Conclusions: This is the first work that attempts to design an explainable machine‐learning tool for the histological diagnosis of solid masses of the ovary. [ABSTRACT FROM AUTHOR]

Echocardiography frequently serves as the first-line treatment of diagnostic imaging for several pathological entities in cardiology. Artificial intelligence (AI) has been growing substantially in information technology and various commercial industries. Machine learning (ML), a branch of AI, has been shown to expand the capabilities and potential of echocardiography. ML algorithms expand the field of echocardiography by automated assessment of the ejection fraction and left ventricular function, integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics. In this review article, we assess the impact of AI and ML in echocardiography. Plain Language Summary: Echocardiography is the most common test in cardiovascular imaging and helps diagnose multiple different diseases. Machine learning, a branch of artificial intelligence (AI), will reduce the workload for medical professionals and help improve clinical workflows. It can rapidly calculate a lot of important cardiac parameters such as the ejection fraction or important metrics during different phases of the cardiac cycle. Machine learning algorithms can include new technology in echocardiography such as speckle tracking, tissue Doppler echocardiography, vector flow mapping, and other approaches in a user-friendly manner. Furthermore, it can help find new subtypes of existing diseases in cardiology. In this review article, we look at the current role of machine learning and AI in the field of echocardiography. [ABSTRACT FROM AUTHOR]

AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis [ABSTRACT FROM AUTHOR]