Modeling Time-On-StreamCatalyst Reactivity in theSelective Hydrogenation of Concentrated Acetylene Streams under IndustrialConditions via Experiments and AI

The description of heterogeneous catalysis is challenged by the intricacy of numerous multi-scale processes that govern the performance of catalyst materials. The chemical environment of the catalytic process and the kinetics of structural changes create configurations of typically unknown local geometries and chemistry. These may result in significant changes in activity or selectivity within minutes, hours, or longer, during the so-called induction period. Here, we use experimental data together with a focused artificial-intelligence (AI) approach based on subgroup discovery and symbolic regression to model the evolution of the catalyst reactivity with time on stream. We consider palladium-based alloys synthesized mechanochemically and applied in the selective hydrogenation of concentrated acetylene streams resulting from a hypothetical electric plasma-assisted methane-to-ethylene process. Our AI approach starts with the identification of descriptions of materials and reaction conditions relevant to acetylene conversion. Then, a model for time-on-stream-dependent selectivity focused on situations associated to noticeable acetylene conversion is obtained by the sure-independence-screening-and-sparsifying-operator (SISSO) approach. Our AI approach identifies relationships between the measured catalyst reactivity and only few, key parameters, from 21 measured and calculated bulk, surface, and mesoscopic materials' properties and reaction parameters offered as candidate descriptive parameters. These identified parameters highlight the crucial influence of surface and subsurface carbon and hydrogen on the selectivity towards ethylene formation. Guided by the AI models, new, highly selective bimetallic and trimetallic systems are designed and tested experimentally.

The Blackboard learning management system (Bb-LMS) is integrated into the nursing educational system to effectively respond to the increasing development of technology in society. The Bb-LMS as an artificial intelligence (AI) approach continuously improves the teaching-learning process of the nursing educational system. Nursing colleges and universities design programs with the assistance of AI tools. Positive perception and impact on the use of Bb-LMS are documented by several authors. Recent studies documented a few challenges of using Bb-LMS including the provision of appropriate training regarding e-learning and the Blackboard system, occasional time delay and audio issues, and the students do not prefer Blackboard in practical nursing subjects. Nonetheless, the predicted influence of the Bb-LMS as an artificial intelligence approach can transform nursing education across all domains of nursing education and practice. The Bb-LMS facilitates university teachers and nursing educators in managing their courses and communicating with their students effectively. Research on artificial intelligence and nursing stressed that the future of artificial intelligence in education envisions preceptors and robots cooperating to produce scholars with fashionable outcomes. Hands-on training and workshops should be conducted for both students and teachers to address the challenges of using the learning management system in nursing education. Future research should consider examining the integration of Bb-LMS in nursing curricular reforms to make nursing education more meaningful and effective. Keywords: Blackboard learning management system, artificial intelligence, nursing education, opportunities in nursing education, prospects in nursing education.

Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of staging in oncology has been the TNM (tumor-node-metastasis) staging system. Though histopathological grading has shown prognostic significance, it is subjective and limited by interobserver variability even among experienced surgical pathologists. Recently, artificial intelligence (AI) approaches have been applied to pathology images toward diagnostic-, prognostic-, and treatment prediction-related tasks in cancer. AI approaches have the potential to overcome the limitations of conventional TNM staging and tumor grading approaches, providing a direct prognostic prediction of disease outcome independent of tumor stage and grade. Broadly speaking, these AI approaches involve extracting patterns from images that are then compared against previously defined disease signatures. These patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (DL)-based representations, which tend to be more abstract. DL approaches have particularly gained considerable popularity because of the minimal domain knowledge needed for training, mostly only requiring annotated examples corresponding to the categories of interest. In this article, we discuss AI approaches for digital pathology, especially as they relate to disease prognosis, prediction of genomic and molecular alterations in the tumor, and prediction of treatment response in oncology. We also discuss some of the potential challenges with validation, interpretability, and reimbursement that must be addressed before widespread clinical deployment. The article concludes with a brief discussion of potential future opportunities in the field of AI for digital pathology and oncology.

Earth system models (ESMs) are our key tools for analyzing the planet's existing state and predicting its evolution in the next continuing human-caused events. However, the use of artificial intelligence (AI) approaches to augment or even replace conventional ESM functions has expanded in recent years, raising hopes that AI will be able to overcome some of the major difficulties in climate research. We address the advantages and disadvantages of neural ESM neurons, as well as the unsolved question of whether AI will eventually replace ESMs. Dynamic geophysical events are the foundation of Earth and environmental studies. Given the widespread acceptance of AI and the growing amount of Earth data, the geoscientific community may wish to seriously explore using artificial intelligence (AI) approaches at a much deeper level. Although it is a tall ambition to integrate hybrid physics and AI approaches from a fresh perspective, geology has yet to figure out how to make such methods feasible. This research is an important step towards realising the concept of combining physics and artificial intelligence to address problems with the Earth's system.

The burden of cardiovascular disease (CVD) is still rising after decades of growth. CVD is the leading cause of death in the Kingdom of Saudi Arabia (KSA), accounting for more than 45% of all CVD-related deaths. The development of cardiac rehabilitation (CR) programs is significantly aided by artificial intelligence (AI) approaches, including machine learning and deep learning models. However, the KSA has a limited supply of CR, and AI methods are unavailable. This review aims to assess contemporary research on AI approaches’ application, potential, and efficacy in CR as a call to action for harnessing it in the KSA. Using the keywords artificial intelligence, AI, machine learning, deep learning models, cardiac rehabilitation, and Saudi Arabia, electronic databases of PubMed, CINHAL, Web of Science, PEDro, and SCOPUS were searched to find relevant articles. Evidence from the literature supports the idea that using AI techniques in CR can improve the ability to effectively diagnose more patients in areas without doctors in Saudi Arabia. Using AI in CR has constrained CR resources, which will lessen the need for outsourcing and enhance healthcare. Patients can receive an accurate diagnosis online thanks to machine learning algorithms and the expanding capabilities of AI.

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

Recent advances in causal machine learning and wider artificial intelligence (AI) methods could provide new insights into the natural histories and potential prevention of clusters of multiple long-term conditions or multimorbidity (MLTC-M). When combined with expertise in clinical practice, applied health research and social science, there is potential to systematically identify and map new clusters of disease, understand the trajectories of patients with these conditions throughout their life course, predict serious adverse outcomes, optimise therapies and consider the influence of wider determinants such as environmental, behavioural and psychosocial factors. The National Institute of Health Research (NIHR) recently funded multidisciplinary consortia to bring together AI specialists, experts in big data and MLTC-M in the first and second waves of this new programme. The so-called AIM consortia of researchers will spearhead the use of artificial intelligence methods and develop insights for the identification and subsequent prevention of MLTC-M. This consensus agreement is aimed at facilitating a community of learning within the AIM consortia, promoting cooperation, transparency and rigour in our approaches while maintaining high methodological standards and consistency in defining and reporting within our research. In bringing together these research collaborations, there is also an opportunity to foster shared learning, synergies and rapidly compare and validate new AI approaches across our respective studies. This step is critical to implementation on the pathway to patient and public benefit. This statement was developed by the first wave of the NIHR AIM consortia and received input from the second wave. It includes representatives across thirteen universities from Edinburgh,

Process control models can be developed by using both conventional and AI approaches. The conventional approaches include regression models and process kinetic models, and the artificial intelligence (AI) approaches are based on artificial neural nets (ANN), genetic algorithm (GA), and fuzzy rule-based expert systems FRBES. Plant data on hot metal desulfurization, carried out by injecting calcium carbide, is analyzed to test and tune different types and combinations of models and then evaluate their relative performance. Although the control models based on process fundamentals provide a fillip to the new ideas for technological developments and improvements, the combination of conventional and AI approaches may be a better option for process control on the shop floor. It is advisable to first develop and test all models and then decide about the best strategy of using them.

Background: Examination protocoling is a resource-intensive task. Various artificial intelligence (AI) approaches have been investigated to automate this process. Objective: To evaluate performance of traditional machine-learning (ML) models, bidirectional encoder representations from transformers (BERT) models, and large language models (LLMs) for automated CT and MRI protocoling. Evidence Acquisition: MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, and Google Scholar databases were searched through July 2025 for studies reporting performance of an AI-based technique in assigning protocols for CT or MRI requisitions. Accuracy results were separately extracted for all models tested in each study and pooled using random-effects meta-analysis. AI approaches were compared using Welch t tests. Common sources of error were qualitatively summarized. Evidence Synthesis: The final analysis included 23 studies, comprising 1,196,259 imaging requisitions. Requisition subspecialties included body imaging (n=4), musculoskeletal imaging (n=3), neuroradiology (n=6), thoracic imaging (n=1), and multiple subspecialties (n=9). Sixteen studies evaluated traditional ML models, eight evaluated BERT models, and five evaluated LLMs. Task-specific model fine-tuning was performed in three studies for traditional ML models, all studies for BERT models, and one study for LLMs. Overall pooled protocoling accuracy was 85% (95% CI: 83-87%). Pooled accuracy was 83% (95% CI: 80-85%) for traditional ML models, 87% (95% CI: 85-89%) for BERT models, and 86% (95% CI: 83-89%) for LLMs; these pooled accuracies were not significantly different between any pairwise combination of the three AI approaches (all p > .05). Among 30 distinct models (14 traditional ML models, nine BERT models, seven LLMs), the top ten performing models comprised two traditional ML models, six BERT models (including the top performing model [BioBert; accuracy, 93%]), and two LLMs. Common sources of error included ambiguous requisition text, data imbalance yielding incorrect protocol assignments for low-volume protocols, presence of multiple clinically reasonable protocols for given requisitions, and difficulty handling requisitions containing terms strongly associated with disparate protocols. Conclusion: The top performing AI models for automated CT and MRI protocoling included predominantly finetuned BERT models. Clinical Impact: AI tools show strong potential to help streamline radiologist workflows, possibly through hybrid AI-radiologist approaches. Fine-tuned LLMs warrant further exploration.

The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers. Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models. The systematic literature review yielded nearly 30 articles meeting the predefined criteria. Analyses of these studies showed that AI methodologies consistently outperformed traditional statistical approaches. In the AI approaches, DL consistently produced the most precise results, while classical ML demonstrated varied performance but maintained high predictive accuracy. The sample size was the recurring factor that increased the accuracy of the predictions for models of the same type. AI and statistical approaches can detect PM developing among patients with gastrointestinal cancers. Therefore, if clinicians integrated these approaches into diagnostics and prognostics, they could better analyze and manage PM, enhancing clinical decision-making and patients' outcomes. Collaboration across multiple institutions would also help in standardizing methods for data collection and allowing consistent results.

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

Integrative health (IH) therapies are increasingly used to manage health conditions, but barriers hindering nurses from offering IH therapies persist. Informatics and artificial intelligence (AI) approaches have potential to promote nurses’ professional use of IH therapies. The purposes of this review are to (1) describe the state of the science of informatics and AI approaches promoting nurses’ use of IH therapies and (2) identify gaps in literature for future investigations. This systematic scoping review followed the systematic review guideline published by the Centre for Review and Dissemination. Five databases were used to retrieve relevant literature published between 2008 and 2018. Sixteen articles describing fourteen studies met predefined eligibility criteria and were reviewed. A descriptive numerical summary method and thematical analysis were used to synthesize the included literature. A fourfold typology emerged to label the informatics and AI approaches, including robots with AI, computer- and mobile-based applications, electronic communication using an information system, and information standards and standardized terminologies. The reviewed studies suggested that informatics and AI approaches could enhance the safety, accessibility, and communication of nursing IH therapies, as well as enable nursing IH data for IH-related knowledge discovery. Several gaps for future research were identified. There is a need of theory-informed research with more rigorous design and a larger scale to provide robust evidence for implementing existing applications and future innovations. Literature suggests progress toward use of informatics and AI approaches to support nurses’ professional use of IH therapies. Further high-quality and theory-informed research is needed regarding the applications used in conventional healthcare in supporting nurses’ use of IH therapies.

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.