Artificial Intelligence-based Tools Research Articles

Statistical and Artificial Intelligence-based Tools for Building Energy Prediction: A Systematic Literature Review

Statistical and Artificial Intelligence-based Tools for Building Energy Prediction: A Systematic Literature Review

An artificial intelligence-based tool for student-generated question

The benefits of student question generation have been extensively established, yet most students do not engage in question-generation activities throughout their formal education and are not used to writing questions. The goal of this project was to create a flexible online learning environment that highlights several types of scaffolding in order to better assist student question production activities in a timely, adaptable, and practically possible manner. The system’s underlying architecture and design principles are outlined. The objective of this work is so utilise Chat-GPT that guides student generated questions. The potential of the student-generated question method of instruction is explored, as is an initial examination of students’ impressions of the helpfulness of the different built-in support systems. Suggestions on how the study’s findings could be used in the classroom and in future research are offered. It has been established that perceived utility plays a significant influence in the dissemination and acceptance of new technology for a wide variety of innovations. Finally, the link between the students’ impressions of the utility of the framework in the established system and their cognitive skills regarding the educational potential of student-generated questions was investigated.

Gender Differences in Perception of Artificial Intelligence-Based Tools

Participation of girls in Science, Technology, Engineering, and Mathematics (STEM) subjects at the higher education (HE) level continues to lag behind that of boys and consequently may affect artificial intelligence proliferation in Ghana. Numerous factors affect girls' engagement in science, one of which is the mistaken belief among girls that STEM subjects are better suited for boys. This study investigates the gender differences in AI-based tools which have become integral for teaching and learning in HE schools. Based on results from 128 students in higher education in Ghana, this article argues that gender is a significant determinant of students’ use of AI-based tools in education. The results further revealed a significant disparity in the overall levels of perceived innovation characteristics based on gender. This study urges managers of Higher Education Institutions (HEIs) to implement policies and measures aimed at facilitating women's engagement and greater familiarity with AI technologies.

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

Cities and buildings represent the core of human life, the nexus of economic activity, culture, and growth. Although cities cover less than 10% of the global land area, they are notorious for their substantial energy consumption and consequential carbon dioxide (CO2) emissions. These emissions significantly contribute to reducing the carbon budget available to mitigate the adverse impacts of climate change. In this context, the designers’ role is crucial to the technical and social response to climate change, and providing a new generation of tools and instruments is paramount to guide their decisions towards sustainable buildings and cities. In this regard, data-informed digital tools are a viable solution. These tools efficiently utilise available resources to estimate the energy consumption in buildings, thereby facilitating the formulation of effective urban policies and design optimisation. Furthermore, these data-driven digital tools enhance the application of algorithms across the building industry, empowering designers to make informed decisions, particularly in the early stages of design. This paper presents a comprehensive literature review on artificial intelligence-based tools that support performance-driven design. An exhaustive keyword-driven exploration across diverse bibliographic databases yielded a consolidated dataset used for automated analysis for discerning the prevalent themes, correlations, and structural nuances within the body of literature. The primary findings indicate an increasing emphasis on master plans and neighbourhood-scale simulations. However, it is observed that there is a lack of a streamlined framework integrating these data-driven tools into the design process.

The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence

Cardiovascular diseases remain the most lethal disorders worldwide. Employing preeminent techniques is paramount for addressing this global challenge. Recent advances in lab-on-a-chip technology have the potential to transform cardiovascular medicine by providing new tools for understanding the biological variability that underlies disease and drug response. Coupling improved fabrication techniques and cardiovascular cellular models with artificial intelligence-based tools for design and analysis primes the field to model and explore cardiovascular biology more accurately than ever before. The fabrication of lab-on-a-chip systems has dramatically evolved with the development of additive manufacturing technologies. Not only can 3D printing be used to fabricate intricate microfluidic networks, but now through the incorporation of smart materials, 4D printing can change the shape and properties of devices based on external stimuli. By leveraging advances in induced pluripotent stem cells, lab-on-a-chip devices open new opportunities within personalized cardiovascular medicine. As customizable systems, devices can be fabricated and populated with donor stem cell-derived cardiovascular cells to mimic the cardiovascular system. These capabilities are only going to improve with the increasingly important role of artificial intelligence in lab-on-a-chip centric design, real-time analysis, data gathering, processing and analysis. Here, we address the potential to personalize cardiovascular medicine through the lens of advances in the fabrication of lab-on-a-chip devices, development of induced pluripotent stem cells derived engineered vasculature and heart tissues, and explore how artificial intelligence continues to shape this field.

Exploring the Impact of Chat-GPT on Teacher Professional Development: Opportunities, Challenges, and Implications

The study discusses the transformative impact of Chat-GPT (Generative Pre-trained Transformer), an artificial intelligence-based tool, in the field of education. Chat-GPT has garnered significant attention and praise for its capabilities in a rapidly evolving technological landscape. However, concerns about deception and misuse have led to bans on educational institutions. Educators' perspectives on Chat-GPT vary, with some viewing it as a threat to students' critical thinking and writing skills. The study emphasises the importance of educators adapting to technological advancements, including AI innovations like Chat-GPT. It posits that Chat-GPT has the potential to revolutionise professional development for educators and better prepare students for the challenges of the future. While initial reactions to new technologies often lean negative, history shows that innovations tend to yield optimistic outcomes. Psychological factors, such as the fear of the unknown and resistance to change, contribute to misaligned expectations. Acknowledging educators' valid concerns, the paper encourages a balanced approach. Educators are advised to integrate AI tools into professional development, leveraging them to enhance productivity and creativity. The literature review conducted in this study reveals valuable insights into the integration of Chat-GPT in the context of teacher professional development via the network of conceptual maps. In conclusion, this study highlights the need for educators to embrace AI as a tool for positive change in education.

A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps

Integrated Pest Management (IPM) is an essential approach used in smart agriculture to manage pest populations and sustainably optimize crop production. One of the cornerstones underlying IPM solutions is pest monitoring, a practice often performed by farm owners by using chromotropic sticky traps placed on insect hot spots to gauge pest population densities. In this paper, we propose a modular model-agnostic deep learning-based counting pipeline for estimating the number of insects present in pictures of chromotropic sticky traps, thus reducing the need for manual trap inspections and minimizing human effort. Additionally, our solution generates a set of raw positions of the counted insects and confidence scores expressing their reliability, allowing practitioners to filter out unreliable predictions. We train and assess our technique by exploiting PST - Pest Sticky Traps, a new collection of dot-annotated images we created on purpose and we publicly release, suitable for counting whiteflies. Experimental evaluation shows that our proposed counting strategy can be a valuable Artificial Intelligence-based tool to help farm owners to control pest outbreaks and prevent crop damages effectively. Specifically, our solution achieves an average counting error of approximately 9% compared to human capabilities requiring a matter of seconds, a large improvement respecting the time-intensive process of manual human inspections, which often take hours or even days.

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.

Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans

The prediction of the therapeutic intensity level (TIL) for severe traumatic brain injury (TBI) patients at the early phase of intensive care unit (ICU) remains challenging. Computed tomography images are still manually quantified and then underexploited. In this study, we develop an artificial intelligence-based tool to segment brain lesions on admission CT-scan and predict TIL within the first week in the ICU. A cohort of 29 head injured patients (87 CT-scans; Dataset1) was used to localize (using a structural atlas), segment (manually or automatically with or without transfer learning) 4 or 7 types of lesions and use these metrics to train classifiers, evaluated with AUC on a nested cross-validation, to predict requirements for TIL sum of 11 points or more during the 8 first days in ICU. The validation of the performances of both segmentation and classification tasks was done with Dice and accuracy scores on a sub-dataset of Dataset1 (internal validation) and an external dataset of 12 TBI patients (12 CT-scans; Dataset2). Automatic 4-class segmentation (without transfer learning) was not able to correctly predict the apparition of a day of extreme TIL (AUC = 60 ± 23%). In contrast, manual quantification of volumes of 7 lesions and their spatial location provided a significantly better prediction power (AUC = 89 ± 17%). Transfer learning significantly improved the automatic 4-class segmentation (DICE scores 0.63 vs 0.34) and trained more efficiently a 7-class convolutional neural network (DICE = 0.64). Both validations showed that segmentations based on transfer learning were able to predict extreme TIL with better or equivalent accuracy (83%) as those made with manual segmentations. Our automatic characterization (volume, type and spatial location) of initial brain lesions observed on CT-scan, publicly available on a dedicated computing platform, could predict requirements for high TIL during the first 8 days after severe TBI. Transfer learning strategies may improve the accuracy of CNN-based segmentation models.Trial registrations Radiomic-TBI cohort; NCT04058379, first posted: 15 august 2019; Radioxy-TC cohort; Health Data Hub index F20220207212747, first posted: 7 February 2022.

Barriers to coach decision-making during Australian football matches and how it can be supported by artificial intelligence

Elite sport coaches are often expected to make high-stakes decisions in time-constrained environments, such as during matches. The quality or efficiency of these decisions is bound by context-specific constraints on their decision-making. Artificial intelligence-based tools could alleviate some of these constraints. In Australian football, it is not yet known what these context-specific constraints are, nor is there any understanding of the thoughts and perception of elite Australian football coaches towards the use of artificial intelligence to support their decision-making. This study is the first to explore these issues in the context of elite Australian football and aims to inform the effective design and integration of tools to support the in-match decision-making of coaches in a manner that is welcomed by coaches. Using semi-structured interviews with elite Australian football coaches ( n = 6) and reflexive thematic analysis, two domains were explored: ‘barriers to decision-making’ and ‘thoughts and perceptions of a decision support system (DSS)’. Coaches discussed seven ‘cognitive’ and ‘environmental’ barriers, some of which may be addressed using artificial intelligence. Meanwhile, four themes were generated for the second domain, namely ‘willingness to adopt’, ‘expectation’, ‘concern and criticism’, and ‘DSS role’. The findings enable practitioners to design tools that are intended to address some of these barriers, while also providing insights into important considerations for the implementation and design of these tools that may increase the likelihood of achieving the endorsement of elite coaches and their support staff.

Açık Uçlu Maddelerin Puanlanmasında ChatGPT ve Gerçek Puanlayıcıların Puanlayıcılar Arası Güvenirlik Bakımından İncelenmesi

The aim of this study is to examine the inter-rater reliability of the responses to open-ended items scored by ChatGPT, an artificial intelligence-based tool, and two real raters according to the scoring keys. The study group consists of 30 students, aged between 13 and 15, studying in Eskişehir province in the 2022-2023 academic year. The data of the study were collected face-to-face with the help of 16 open-ended items selected from the sample questions published in the International Student Assessment Program-PISA Reading Skills. Correlation, percentage of agreement and the Generalizability theory were used to determine inter-rater reliability. SPSS 25 was used for correlation analysis, Excel for percentage of agreement analysis, and EduG 6.1 for the Generalizability theory analysis. The results of the study showed that there was a positive and high level of correlation between the raters, the raters showed a high level of agreement, and the reliability (G) coefficients calculated using the Generalizability theory were lower than the correlation values and percentage of agreement. In addition, it was determined that all raters showed excellent positive correlation and full agreement with each other in the scoring of the answers given to the short-answer items whose answers were directly in the text. In addition, according to the results of the Generalizability theory, it was found out that the items (i) explained the total variance the most among the main effects and the student-item interaction (sxi) explained the most among the interaction effects. As a result, it can be suggested to educators to get support from artificial intelligence-based tools such as ChatGPT when scoring open-ended items that take a long time to score, especially in crowded classes or when time is limited.

A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal.

To develop and validate a novel artificial intelligence (AI) tool for automated segmentation of mandibular incisive canal on cone beam computed tomography (CBCT) scans. After ethical approval, a data set of 200 CBCT scans were selected and categorized into training (160), validation (20), and test (20) sets. CBCT scans were imported into Virtual Patient Creator and ground truth for training and validation were manually segmented by three oral radiologists in multiplanar reconstructions. Intra- and interobserver analysis for human segmentation variability was performed on 20% of the data set. Segmentations were imported into Mimics for standardization. Resulting files were imported to 3-Matic for analysis using surface- and voxel-based methods. Evaluation metrics involved time efficiency, analysis metrics including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Root mean square error (RMSE), precision, recall, accuracy, and consistency. These values were calculated considering AI-based segmentation and refined-AI segmentation compared to manual segmentation. Average time for AI-based segmentation, refined-AI segmentation and manual segmentation was 00:10, 08:09, and 47:18 (284-fold time reduction). AI-based segmentation showed mean values of DSC 0.873, IoU 0.775, RMSE 0.256 mm, precision 0.837 and recall 0.890 while refined-AI segmentation provided DSC 0.876, IoU 0.781, RMSE 0.267 mm, precision 0. 852 and recall 0.902 with the accuracy of 0.998 for both methods. The consistency was one for AI-based segmentation and 0.910 for manual segmentation. An innovative AI-tool for automated segmentation of mandibular incisive canal on CBCT scans was proofed to be accurate, time efficient, and highly consistent, serving pre-surgical planning.

Urologist validation of an artificial intelligence-based tool for automated estimation of penile curvature

Severity of penile curvature (PC) is commonly used to select the optimal surgical intervention for hypospadias, either alone or in conjunction with other phenotypic characteristics. Despite this, current literature on the accuracy and precision of different PC measurement techniques in hypospadias patients remains limited. Assess the feasibility and validity of an artificial intelligence (AI)-based model for automatic measurement of PC. Seven 3D-printed penile models with variable degrees of ventral PC were used to evaluate and compare interobserver agreement in estimation of penile curvatures using various measurement techniques (including visual inspection, goniometer, manual estimation via a mobile application, and an AI-based angle estimation app. In addition, each participant was required to complete a questionnaire about their background and experience. Thirty-five clinical practitioners participated in the study, including pediatric urologists, pediatric surgeons, and urologists. For each PC assessment method, time required, mean absolute error (MAE), and inter-rater agreement were assessed. For goniometer-based measurement, the lowest MAE achieved was derived from a model featuring 86° PC. When using either UVI (unaid visual inspection), mobile apps, or AI-based measurement, MAE was lowest when assessing a model with 88° PC, indicating that high-grade cases can be quantified more reliably. Indeed, MAE was highest when PC angle ranged between 40° and 58° for all the investigated measurement tools. In fact, among these methodologies, AI-based assessment achieved the lowest MAE and highest level of inter-class correlation, with an average measurement time of only 22s. AI-based PC measurement models are more practical and consistent than the alternative curvature assessment tools already available. The AI method described in this study could help surgeons and hypospadiology researchers to measure PC more accurately.

The Role of Patients’ Advocacy Groups to Reduce Headache Pain in the European Union: from the Italian Case History to the European Migraine Action Plan

From the “patients’ right to avoid unnecessary suffering and pain” to the “Charter of Rights of the Cephalalgic Patient”, over the years the role of the civic & patients’ organisations to reduce headache pain in the European Union is increased: this research article explores the experiences of individuals affected by migraines and headaches, highlighting challenges, rights, and the need for improved healthcare support. Based on an extensive civic survey conducted at the national level by the Italian NGO Cittadinanzattiva, the study reveals the profound impact of headaches on people's lives, emphasising the lack of disability recognition and difficulties in accessing quality care. Through compelling testimonies, the article underscores the emotional, psychological, and physical burden of these conditions, and describes an innovative artificial intelligence-based tool called “EMI”. Together with many actors, since decades Cittadinanzattiva has been engaged in the fight against chronic pain for decades, both at the national and European levels. In this regard, what attention will the new MEPs (Members of the European Parliament) and the new leadership of the EU Commission DG Santé (Directorate-General for Health and Food Safety) dedicate to this issue in the wake of the upcoming European elections in 2024? From various quarters, there is a call for a European Migraine Action Plan aimed at finally placing this issue on the agenda of European political institutions, promoting a more inclusive and patient-centered vision within the European healthcare landscape, where the pain of those suffering from migraines and headaches is no longer invisible or unheard.

Chest X-ray Foreign Objects Detection Using Artificial Intelligence.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study.

Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8)months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3months, and APRI score at 3months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.

Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT

Software development processes are continuously evolving and rapidly transforming alongside the rapid changes in technology. Recently, innovations in the field of Artificial Intelligence (AI) have led to significant changes in software development practices. AI tools can greatly enhance traditional software development processes by offering developers the ability to create projects more intelligently, swiftly, and effectively. These tools can be employed in various tasks, such as code generation, test automation, error analysis, and performance improvements. Particularly, ChatGPT, an AI-based language model that has had a profound impact on almost every domain, can assist software developers in writing code faster and in a more natural language manner. In this research article, essential information about the usage of ChatGPT in the software development process is presented. To evaluate some capabilities of ChatGPT in the software development context, applications were performed on a software project. For this purpose, a software development process was constructed based on the responses provided by ChatGPT. Various questions related to software development processes were formulated, and the responses generated by GPT were evaluated. The obtained results indicated that ChatGPT exhibited excellent performance in the software development process. Based on these findings, it was observed that AI-based models like ChatGPT could be effectively utilized as assisting tools in software development processes, accelerating traditional workflows. Furthermore, AI-based tools can automate testing processes, enhancing software quality while saving time and effort.

Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods

Artificial Intelligence is a sub-branch of artificial intelligence used to produce new data or content. These methods can create recent examples in different categorical fields such as natural language processing, image processing, music, and video creation by using models from learning clusters with artificial intelligence (AI) tools in this field. AI tools that can solve real-world problems are also created using different methods apart from generative AI methods. With generative-based artificial intelligence tools, it can facilitate people's work in jobs that require creativity. However, they can offer the opportunity to build advanced models that learn from data with other artificial intelligence methods. In the study, the public dataset has been used. This dataset includes trending artificial intelligence tools, AI methods, and user scores. In this study the working area and user trend of the ai tools in the dataset and the effect of generative AI methods on the development of the tool are discussed. Random Forest and Naive Bayes algorithms from classification methods have been used to measure the impact and estimation. Several AI tools help solve real-life problems. Identifying what type of category is needed for AI tools and method selection are interlinked, and the research provides an overview of this connection.

Artificial Intelligence in Education: Literature Review on The Role of Conversational Agents in Improving Learning Experience

A growing number of teachers and students are turning to artificial intelligence-powered conversational bots as a means of bolstering and facilitating learning and teaching. Conventional educational agents are intended to do the duties that would normally be carried out by human educators, specifically, providing students with instructions. The purpose of this research is to review the literature on the use of chatbots and conversational agents to promote learning and enhance student engagement. To better understand the complexities of deploying conversational agents for classroom use, it is helpful to first recognize the role that conversational technologies play in facilitating student engagement during learning. The study found credible evidence to support the need for conversational agents in education as teachers grapple with urgency to implement artificial intelligence-based tools and services in classroom and online learning.

Conversation between a clinical biologist and an artificial intelligence on prostate cancer biomarkers: a critical reading.

Artificial intelligence is increasingly used in the field of medicine as a diagnostic aid, particularly for image analysis and more generally for data processing. Many artificial intelligence-based tools have been specifically developed for clinical biology, but some more general ones can help to improve the dissemination of medical knowledge. To test whether and to what extent an automated conversation tool could answer questions on a clinical biology topic (i.e. prostate cancer biomarkers), we questioned ChatGPT, an artificial intelligence-powered model dedicated to optimize language models for dialogue. Then we analyzed its responses.