Potential Applications Of Artificial Intelligence Research Articles

The potential application of artificial intelligence in veterinary clinical practice and biomedical research.

Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

ChatGPT and Rhinoplasty Recovery: An Exploration of AI's Role in Postoperative Guidance.

The potential applications of artificial intelligence (AI) in health care have garnered significant interest in recent years. This study presents the first published exploration of ChatGPT, an AI language model, as a tool for providing postoperative guidance during rhinoplasty recovery. The primary objective was to shed light on the role of ChatGPT in augmenting patient care during the critical postoperative phase. Using the Rhinobase database, standardized questions were formulated to evaluate AI-generated responses addressing pain management, swelling, bruising, and potential asymmetries. Results demonstrated that ChatGPT has the potential to enhance patient education and alleviate emotional distress by providing general information and reassurance during the recovery process. However, the study emphasized that AI should not replace personalized advice from qualified health care professionals. This pioneering investigation offers valuable insights into the integration of AI and human expertise, paving the way for optimized postrhinoplasty recovery care.

Artificial Intelligence in battling infectious diseases: A transformative role.

It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.

Sztuczna inteligencja w dydaktyce języków obcych (perspektywa interdyscyplinarna)

The aim of this article is to present the possibilities of utilizing artificial intelligence (AI) in foreign language didactics, as AI increasingly influences the methods of teaching and learning. The research findings presented, conducted in recent years by scientists from various scientific disciplines, pertain to the potential applications of AI in education and foreign language didactics. They indicate the strengths and weaknesses of AI implementation, as well as the opportunities and risks associated with utilizing and integrating AI into the educational process. The author describes the most frequently mentioned characteristics of AI that support language education and focuses on those features of AI systems that are teacher-oriented, assist in their work, and allow for the individualization of teaching methods.

Smart Diagnosis of Urinary Tract Infections: is Artificial Intelligence the Fast-Lane Solution?

Artificial intelligence (AI) can significantly improve physicians' workflow when examining patients with UTI. However, most contemporary reviews are focused on examining the usage of AI with a restricted quantity of data, analyzing only a subset of AI algorithms, or performing narrative work without analyzing all dedicated studies. Given the preceding, the goal of this work was to conduct a mini-review to determine the current state of AI-based systems as a support in UTI diagnosis. There are sufficient publications to comprehend the potential applications of artificial intelligence in the diagnosis of UTIs. Existing research in this field, in general, publishes performance metrics that are exemplary. However, upon closer inspection, many of the available publications are burdened with flaws associated with the improper use of artificial intelligence, such as the use of a small number of samples, their lack of heterogeneity, and the absence of external validation. AI-based models cannot be classified as full-fledged physician assistants in diagnosing UTIs due to the fact that these limitations and flaws represent only a portion of all potential obstacles. Instead, such studies should be evaluated as exploratory, with a focus on the importance of future work that complies with all rules governing the use of AI. AI algorithms have demonstrated their potential for UTI diagnosis. However, further studies utilizing large, heterogeneous, prospectively collected datasets, as well as external validations, are required to define the actual clinical workflow value of artificial intelligence.

Wine quality analysis by the structural causal model (SCM)

Bayes network modelling for structural causal analysis between wine physicochemical data and quantitative human quality blind assessments is applied. The large dataset of white and red "Vinho Verde'' wine samples from Portugal, which was available from an open data repository for machine learning at the University of California at Irving, was analysed. The dataset contains 4898 white and 1599 red samples evaluated by blind tastes by a minimum of 3 sensory assessors and 12 physicochemical properties. The casual effects of wine analytic data on human quality evaluations are evaluated numerically by Bayes neural networks for adjusted sets of the covariates as marginal distributions and presented graphically as partial dependence plots. Structural causal analysis revealed important differences between the most important variables for quality predictions and the individual causal effects. Bayes neural network models of the partial dependencies show more pronounced nonlinear effects for red wines compared to white wine quality. The artificial intelligence models with boosted random decision tree forests for untrained wine samples yield a 5% relative standard error of predictions compared to 12% for the linear models and ordinary least squares estimation. For red wine, the most important direct causal quality effects are caused by alcohol, volatile acidity, and sulphates. Alcohol improves quality with a maximum plateau at 14%, while volatile acidity has a strong proportional negative effect. The effect of sulphates is highly nonlinear with maximum positive effect at a concentration of 1 g/L of K2SO4. For the white wine samples causal effects are linear with positive effects of alcohol and negative effects of volatile and fixed acidity. The developed structural causal model enables evaluation of targeted wine production interventions, named as “doing x, do(x) models”, as restructured adjusted Bayes networks. It leads to potential applications of artificial intelligence in wine production technology and process quality control.

Artificial Intelligence Techniques in Bioinformatics: Unravelling Complex Biological Systems

This paper reviews the integration of Artificial Intelligence (AI) techniques with bioinformatics, focusing on its applications in absorbing large amounts of biological data and understanding complex biological systems. It covers various AI paradigms, including data mining, machine learning, deep learning, and adaptive algorithms, and their applications in drug discovery, functional genomics, targeted medicine, protein structure prediction, and genomic sequence analysis. The paper emphasizes the role of AI algorithms and biological data in improving knowledge extraction, pattern recognition, and predictive modelling in natural settings. Furthermore, this research evaluates the difficulties and potential applications of AI in bioinformatics, including limitations with data quality, AI model interpretability, integrating multi-omics data, and ethical issues. In summary, this review fabricates the most recent state-of-the-art AI approaches in bioinformatics and offers researchers, practitioners, and stakeholders a road map for utilizing AI developments to effectively decipher biological systems' complexity and produce groundbreaking discoveries and medical applications

New Horizons in artificial intelligence in the healthcare of older people.

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Exploratory study on student perception on the use of chat AI in process-driven problem-based learning

IntroductionWith the increasing prevalence of artificial intelligence (AI) technology, it is imperative to investigate its influence on education and the resulting impact on student learning outcomes. This includes exploring the potential application of AI in process-driven problem-based learning (PDPBL). This study aimed to investigate the perceptions of students towards the use of ChatGPT) build on GPT-3.5 in PDPBL in the Bachelor of Pharmacy program. MethodsEighteen students with prior experience in traditional PDPBL processes participated in the study, divided into three groups to perform PDPBL sessions with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data searching and problem-solving. Questionnaires were used to collect data on the impact of ChatGPT on students' satisfaction, engagement, participation, and learning experience during the PBL sessions. ResultsThe survey revealed that ChatGPT improved group collaboration and engagement during PDPBL, while increasing motivation and encouraging more questions. Nevertheless, some students encountered difficulties understanding ChatGPT's information and questioned its reliability and credibility. Despite these challenges, most students saw ChatGPT's potential to eventually replace traditional information-seeking methods. ConclusionsThe study suggests that ChatGPT has the potential to enhance PDPBL in pharmacy education. However, further research is needed to examine the validity and reliability of the information provided by ChatGPT, and its impact on a larger sample size.

Adaptive digital twin for pressure swing adsorption systems: Integrating a novel feedback tracking system, online learning and uncertainty assessment for enhanced performance

This paper presents a novel approach to digitizing and modeling pressure swing adsorption (PSA) processes using an uncertainty-aware digital twin. PSA modeling presents unique challenges due to its complex and cyclic behavior, which lacks a steady state. By contributing to the literature on periodic systems, we provide valuable insights into the potential applications of artificial intelligence and digital twins beyond the field of cyclic processes. Our proposed methodology can enhance the understanding and optimization of complex systems across various industries and applications. The proposed digital twin is uncertainty-aware and reliable, continuously updating itself through online learning and utilizing a novel feedback tracker to accurately represent the PSA system. This robust and adaptable methodology supports optimal PSA system operation and facilitates informed decision-making for enhanced process operation. The results demonstrate that the proposed approach yields a reliable digital twin for the PSA unit, capable of tracking the process’s complex dynamics and adapting to changes, including adsorbent degradation, which is a significant challenge in PSA operations. Overall, this work highlights the potential of advanced technologies, such as digital twins and artificial intelligence, to improve performance and efficiency in the field of process engineering. This work contributes to the ongoing efforts to optimize industrial processes and support sustainable development by providing a reliable and adaptable methodology for digitizing PSA processes.

A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato‐oncology

AbstractBackgroundThe potential applications of artificial intelligence (AI) in dermatology are evolving rapidly. Chatbots are an emerging trend in healthcare that rely on large language models (LLMs) to generate answers to prompts from users. However, the factuality and user experience (UX) of such chatbots remain to be evaluated in the context of dermato‐oncology.ObjectivesTo examine the potential of Chat Generative Pretrained Transformer (ChatGPT) as a reliable source of information in the context of actinic keratosis (AK) and to evaluate clinicians' attitudes and UX with regard to the chatbot.MethodsA set of 38 clinical questions were compiled and entered as natural language queries in separate, individual conversation threads in ChatGPT (OpenAI, default GPT 3.5). Questions pertain to patient education, diagnosis, and treatment. ChatGPT's responses were presented to a panel of 7 dermatologists for rating of factual accuracy, currency of information, and completeness of the response. Attitudes towards ChatGTP were explored qualitatively and quantitatively using a validated user experience questionnaire (UEQ).ResultsChatGPT answered 12 questions (31.6%) with an accurate, current, and complete response. ChatGPT performed best for questions on patient education, including pathogenesis of AK and potential risk factors, but struggled with diagnosis and treatment. Major deficits were seen in grading AK, providing up‐to‐date treatment guidance, and asserting incorrect information with unwarranted confidence. Further, responses were considered verbose with an average word count of 198 (SD 55) and overly alarming of the risk of malignant transformation. Based on UEQ responses, the expert panel considered ChatGPT an attractive and efficient tool, scoring highest for speed of information retrieval, but deemed the chatbot inaccurate and verbose, scoring lowest for clarity.ConclusionsWhile dermatologists rated ChatGPT high in UX, the underlying LLMs that enable such chatbots require further development to guarantee accuracy and concision required in a clinical setting.

Artificial Intelligence in the Prevention and Detection of Cardiovascular Disease.

For more than 60 years, artificial intelligence (AI) has served as a mainstay in augmenting and assisting the lives of individuals across a wide array of interests and professional fields. Functioning to create deep computer simulations, analyze data, solve problems, and synthesize human behavior/emotion, AI has recently become a topic of popular interest in many fields of medicine. Despite decades of usage, modern AI-and its newer branch of machine learning (ML)-have yet to find a fully established and regulated niche in medicine. Understanding the clinical implications that AI and ML might be able to play in cardiovascular medicine, studies have sought to understand and compare how this technology compares with human rationality and diagnostics. Utilizing AI and ML in an array of cardiovascular medical techniques, analyses, and predictive measurements seems to have produced accurate results while also saving healthcare providers time and enabling them to expand their reach to further populations. Although current research and literature might hypothesize AI's potential clinical applications, it is nearly impossible to fully understand the breadth and scope that this new technology can play in the future. In this article, we attempt to analyze a few of the potential applications of AI and ML for the detection, prevention, and treatment of cardiovascular disease. Additionally, we discuss how AI might make cardiovascular care more equitable and highlight a few precautions for utilizing this technology.

Artificial Intelligence in Human Resource Management: Advancements, Implications and Future Prospects

The present condition, challenges, and potential applications of artificial intelligence (AI) in human resource management (HRM) are all explored in this survey article. As an innovation, artificial intelligence (AI) has the potential to completely revolutionize several facets of human resource management (HRM). Examining the usage of AI-powered tools and systems in different HR processes, the present situation with AI in HRM is examined. These encompass learning and development, performance management, employee engagement, and recruiting. The use of AI algorithms and machine learning approaches to automate regular HR operations, analyze vast amounts of employee data, and provide insightful data to aid decision-making is addressed in this article. However, integrating AI into HRM also poses a number of difficulties that must be resolved. Bias, privacy issues, and transparency are just a few of the ethical and legal ramifications of using AI in decision-making processes that are discussed in this survey. The study emphasizes how accountability and fairness must be maintained in AI systems by responsible design, oversight, and periodic evaluation. With an emphasis on job displacement and workforce reorganization, the possible influence of AI on the human workforce is also explored. To effectively traverse this change, strategies including work role redefinition, employee up skilling, and establishing a collaborative atmosphere between humans and AI are suggested. The possible advantages and breakthroughs that AI might bring to HRM practices are highlighted as the future perspectives of AI in HRM are examined. As new applications for AI in HRM, sentiment analysis, predictive analytics, intelligent decision support, and personalized employee experiences are all highlighted. In order to fully realize the promise of AI in HRM, the study underlines the significance of data infrastructure, data governance frameworks, and a data-driven culture. Overall, this survey study offers an in-depth review of the existing situation, difficulties, and prospects for AI in HRM. It aggregates current information, identifies research gaps, and gives practitioners and scholars new perspectives on how AI will fundamentally alter the way HRM activities are carried out in the future.

Report on the conference "Ius ex silico. Artificial Intelligence in the Service of Justice"

On April 17-18, 2023, in Warsaw, the conference 'Ius ex silico: Artificial Intelligence in the Service of Justice' took place. The choice of this topic was not random, as artificial intelligence (AI) is currently one of the most widely discussed subjects worldwide and has become an essential element of supporting solutions in significant societal issues, including healthcare, security, and the justice system. In the latter area, it can contribute to expediting proceedings, facilitating case management, and enhancing the quality of judgments. Therefore, the Ministry of Justice (referred to as MS), recognizing the importance of this topic, decided to organize the conference titled 'Ius ex silico: Artificial Intelligence in the Service of Justice.' The organizers aimed to intensify discussions about potential applications of artificial intelligence in the Polish justice system, gather and share knowledge and prior experiences with participants. Additionally, the conference served as an attempt to answer which of the selected digital solutions should be implemented first.

Artificial intelligence in logistics and supply chain management: A primer and roadmap for research

AbstractThe dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain.

Physics-Based Swab and Surge Simulations and the Machine Learning Modeling of Field Telemetry Swab Datasets

Drilling operations are the major cost factor for the oil industry. Appropriately designed operations are essential for successful drilling. Optimized drilling operations also enhance drilling performance and reduce drilling costs. This is achieved by increasing the bit life (minimizing premature bit wear), drilling more quickly, which reduces drilling time, and also reducing tripping operations. This paper is presented in two parts. The first part compares the parametric physics-based swab and surge simulation results obtained from the Bingham plastic, power law, and Robertson–Stiff models. The aim is to show how the model’s predictions deviate from each other. Two 80:20 oil/water ratio (OWR) oil-based drilling fluids and two 90:10 OWR oil-based drilling fluids, 1.96 sg and 2.0 sg, were considered in vertical and deviated wells. Analysis of the simulation results revealed that the deviations depend on the drilling fluid’s physical and rheological parameters as well as the well trajectory. Moreover, the model’s predictions were inconsistent. Data-driven machine learning (ML) modeling is the focus of the second section. Data-driven modeling was performed using both software-generated datasets and field datasets. The results show that the random forest regressor (RF), artificial neural network (ANN), long short-term memory (LSTM), LightGBM, XGBoost, and multivariate regression models predicted the training and test datasets with higher R-squared and minimum mean square error values. Deploying the ML model in real-time applications and the planning phase would lead to potential applications of artificial intelligence for well planning and optimization processes.

Artificial Intelligence and Machine Learning in Rotator Cuff Tears

Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.

AI AND ONLINE DISPUTE RESOLUTION: MEDIATION

The paper will address the problem of replacing human in dispute resolution taking Mediation as an example. The widespread use of internet and online technology has led to the emergence of various e-commerce businesses across different sectors of the economy, resulting in a constant flow of transactions. The importance of the internet in the virtual realm cannot be denied, and this has led to the emergence of virtual disputes that require virtual settlement mechanisms. Online Dispute Resolution (ODR) is a form of Alternative Dispute Resolution (ADR) that has arisen to address this issue. This paper aims to examine the potential applications of Artificial Intelligence (AI) in ODR, specifically in the context of transnational disputes to AI instead of Human interference. This paper will state the analysis methodology used in studying the topic The incorporation of AI into ODR systems has the potential to accelerate the settlement of low-value transnational claims. ODR typically involves negotiation or settlement during mediation or arbitration using video, audio, or written collaboration software, or any other digital platform where parties can come together to resolve their disputes.

Potential applications of Artificial Intelligence in Surgery

Potential applications of Artificial Intelligence in Surgery