Application Of Artificial Intelligence Research Articles

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Designing experiential learning activities with generative artificial intelligence tools for authentic assessment

Purpose This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning. Design/methodology/approach The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use. Findings The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes. Originality/value This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

Contextual knowledge graph approach to bias-reduced decision support systems

ABSTRACT Recent research shows that automated decision-making systems based on Artificial Intelligence (AI) may lead to perceived unfairness and bias, especially in some sensitive areas. There is no universal debiasing solution for AI applications and systems. This paper proposes a bias-reducing framework based on contextual knowledge graphs for decision-making systems to help analyse and detect potential bias factors during system operation in near-real time. In particular, the contextual knowledge graph is designed to learn the relations between current tasks and corresponding features and explore the correlation among data, context and tasks. Three bias assessment metrics (i.e., label bias, sampling bias and timeliness bias) are proposed to measure, quantify and qualitatively define the bias level for the pre- and post-modelling. The trained model using debiased datasets combines contextual knowledge to support fairer decision-making. Experimental results show that the proposed method is more effective in supporting fairer decision- making than the existing methods.

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.

Artificial Intelligence in Enhancing the Kosovo Health Information System

The contemporary healthcare landscape faces unprecedented challenges, ranging from data fragmentation within health information systems to the need for timely and accurate diagnostics. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the Kosovo Health Information System (HIS). By leveraging the capabilities of AI, we aim to address existing limitations in data interoperability, predictive analytics, and personalized healthcare. The study incorporates a comprehensive literature review, methodological data collection, and analysis of the current state of the Kosovo HIS. Drawing inspiration from successful global implementations, we delve into the possibilities of AI applications in diagnosis, treatment personalization, and population health management. The research also examines ongoing initiatives and collaborations aimed at integrating AI into the Kosovo HIS. Through a critical assessment of technical challenges and ethical considerations, the paper provides insights into the opportunities and hurdles associated with the implementation of AI in healthcare. Ultimately, this research contributes to the discourse on the future prospects of healthcare in Kosovo, highlighting the potential long-term impacts of AI integration and offering recommendations for advancing the country's health information infrastructure.   Received: 5 February 2024 / Accepted: 23 April 2024 / Published: 5 May 2024

Advancement in AI-Based Devices for Monitoring Heart Health in Patients with Cardiac Conditions

In the contemporary landscape of cardiac healthcare, the integration of Artificial Intelligence (AI) into monitoring devices represents a groundbreaking shift towards enhancing patient outcomes and streamlining the management of heart health. This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences. Through a meticulous selection and analysis of current studies, we uncover the transformative capabilities of AI technologies in detecting and predicting heart-related anomalies, offering a personalized approach to cardiac care. The findings underscore the paramount importance of AI in facilitating early intervention, reducing the incidence of heart-related morbacies, and ultimately, charting a course towards a more responsive and adaptive healthcare ecosystem. The significance of this review lies not only in its comprehensive synthesis of existing research but also in its exploration of future potentials, highlighting the ongoing evolution of AI as a pivotal ally in the quest for improved heart health monitoring. This paper aims to contribute to the academic and medical discourse surrounding AI applications in healthcare, offering insights that may guide future innovations and research directions. Keywords--- AI-driven Cardiac Monitoring, Wearable Health Technology, Remote Heart Health Monitoring, Smart Health Devices, Cardiovascular Disease Management, Machine Learning in, Cardiology, Digital Health Solutions, Real-time Heart Health Analytics, Sensor Technology in Cardiac Care, Patient-Centric Heart Monitoring, AI-based Cardiac Diagnostics, Continuous Heart Rate Monitoring, Mobile Health Applications, Personalized Cardiac Care, Telemedicine in Cardiology.

Impact of Artificial Intelligence in Accounting

The application of artificial intelligence (AI) in accounting has transformed conventional methods and changed how financial management and decision-making are conducted. This essay examines the complex effects of artificial intelligence (AI) on accounting, focusing on how these effects may affect organizational efficiency, accuracy, and strategic decision-making. This study investigates the use of artificial intelligence (AI) technologies, including as machine learning, natural language processing, and robotic process automation, in accounting operations, from data input and analysis to risk assessment and forecasting, through a thorough evaluation of the body of existing literature. It also looks at the potential and problems that come with implementing AI, such as labor reskilling, ethical issues, and regulatory compliance. This research gives insightful information about the transformative potential of AI in accounting by combining findings from academic studies and industry sources. It also includes tips for practitioners and policymakers navigating this ever-changing field. The economy, science, and technology are developing at a rapid pace, ushering in the age of artificial intelligence, which has had a profound impact on every part of life. Whether or not there is widespread worry about the fate of accountants facing elimination. This essay will examine how artificial intelligence will affect accounting staff and how to prevent accounting fraud while also producing positive effects on the quality of accounting information. Since machines cannot make decisions, artificial intelligence won't result in widespread unemployment. The article's conclusion will emphasize that, in the grand scheme of artificial intelligence, accounting staff members should develop their own seven skill areas and become fully qualified professionals.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

How artificial intelligence can provide information about subdural hematoma: Assessment of readability, reliability, and quality of ChatGPT, BARD, and perplexity responses.

Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about "Subdural Hematoma." In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about "Subdural Hematoma" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of "Subdural Hematoma" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.

Application of artificial intelligence in mine ventilation: a brief review

In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.

New advances in artificial intelligence applications in higher education?

New advances in artificial intelligence applications in higher education?

New Frontiers in Breast Cancer Imaging: The Rise of AI

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Artificial intelligence to enhance prehospital stroke diagnosis and triage: a perspective

As health systems organize to deliver the highest quality stroke care to their patients, there is increasing emphasis being placed on prehospital stroke recognition, accurate diagnosis, and efficient triage to improve outcomes after stroke. Emergency medical services (EMS) personnel currently rely heavily on dispatch accuracy, stroke screening tools, bypass protocols and prehospital notification to care for patients with suspected stroke, but novel tools including mobile stroke units and telemedicine-enabled ambulances are already changing the landscape of prehospital stroke care. Herein, the authors provide our perspective on the current state of prehospital stroke diagnosis and triage including several of these emerging trends. Then, we provide commentary to highlight potential artificial intelligence (AI) applications to improve stroke detection, improve accurate and timely dispatch, enhance EMS training and performance, and develop novel stroke diagnostic tools for prehospital use.

IMPACT OF ARTIFICIAL INTELLIGENCE ON SOCIETY: RISK AND CHALLENGES

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These machines are designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI technologies include machine learning, which enables computers to learn from experience and improve their performance over time without being explicitly programmed, and deep learning, which uses neural networks to process complex patterns and data. AI has applications in various fields, including healthcare, defence, transportation, education, and labour market, and its development has raised ethical, social, educational, and economic implications. The main objective of the study is to explore the literature on the artificial intelligence, its impact on society as well as challenges and risks posed by AI. Recently AI has been utilized in various fields. To cover various societal aspects, literature has been reviewed from various disciplines where AI is applied. These areas include healthcare, automobiles, education, labour market, defence, entertainment, computation, and security. These articles are retrieved from peer-reviewed sources based on the keywords suggesting the role of AI, forecasting &amp; assessment of impact, behavioural &amp; ecological aspects of AI, and AI's relation to employment. In this study it has been concluded that application of Artificial Intelligence has transformed the conventional ways of almost every area of modern society by bringing the significant changes in the industry, commerce, defence, education, labour market and healthcare services. But it has raised certain concerns also such as automation leads to job displacement and economic inequality concentrating revenue among fewer individuals. AI supported Autonomous Weapons to kill could cause mass casualties if misused. Hence the idea is both devastating and exciting and the application of AI should be carefully monitored. 

Applications of Artificial Intelligence in Surgery: clinical, technical, and governance considerations.

Artificial intelligence (AI) will power many of the tools in the armamentarium of digital surgeons. AI methods and surgical proof-of-concept flourish, but we have yet to witness clinical translation and value. Here we exemplify the potential of AI in the care pathway of colorectal cancer patients and discuss clinical, technical, and governance considerations of major importance for the safe translation of surgical AI for the benefit of our patients and practices.

Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review

ABSTRACT Artificial intelligence (AI) is the development of computer systems that can do tasks that normally require human intelligence. A number of dental specialties, including pediatric dentistry, now use AI and its subsets, machine learning, and deep learning. The evolution of AI in healthcare has been linked to the creation of AI applications meant to support medical professionals in diagnosing patients and choosing the best course of treatment. AI is the capability of robots to learn and use that information to carry out a range of cognitive tasks, including language processing, learning, reasoning, and making decisions—basically imitating human behavior. This review gives an overview of the numerous applications of AI that are beneficial to pediatric dentistry.

Phase analysis: a novel and useful application of artificial intelligence in endoscopy

Phase analysis: a novel and useful application of artificial intelligence in endoscopy

Application of artificial intelligence in hypertension.

Hypertension is an important modifiable risk factor for morbidity and mortality associated with cardiovascular disease. The incidence of hypertension is increasing not only in Korea but also in many Western countries due to the aging of the population and the increase in unhealthy lifestyles. However, hypertension control rates remain low due to poor adherence to antihypertensive medications, low awareness of hypertension, and numerous factors that contribute to hypertension, including diet, environment, lifestyle, obesity, and genetics. Because artificial intelligence (AI) involves data-driven algorithms, AI is an asset to understanding chronic diseases that are influenced by multiple factors, such as hypertension. Although several hypertension studies using AI have been published recently, most are exploratory descriptive studies that are often difficult for clinicians to understand and have little clinical relevance. This review aims to provide a clinician-centered perspective on AI by showing recent studies on the relevance of AI for patients with hypertension. The review is organized into sections on blood pressure measurement and hypertension diagnosis, prognosis, and management.

Artificial intelligence-based prediction models of bio-treated sand strength for sustainable and green infrastructure applications

As a sustainable, environmentally friendly, and green solution, biomineralization/bio-treatment process has recently emerged for enhancing the strength characteristics of loose sands by inducing cementation material commonly referred to as microbially induced calcite/cementation precipitation (MICP). To date, existing research on the unconfined compression strength of MICP-treated sands (UCSmicp) relies heavily on experimental data, with predictions primarily based on linear or curvilinear models that consider calcite (CaCO3) as the main influencing factor. However, there is limited exploration into the application of artificial intelligence (AI) and machine learning (ML) methods for UCSmicp prediction involving multiple influencing factors and their validation on unseen laboratory data. Therefore, this study intends to propose new prediction models of UCSmicp using four advanced ML techniques: Gaussian Process Regression (GPR), Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Gene Expression Programming (GEP). The models incorporate various factors affecting UCSmicp, including sand uniformity coefficient (Cu), initial void ratio (e), mean grain size (D50), optical density at 600 mm wavelength (OD600), urea concentration (U), calcium concentration (Ca), and calcite content (C). Performance evaluation of models involved statistical assessments, error analysis, cross validation on laboratory data, and sensitivity and monotonicity studies. Results indicate that the proposed models exhibit exceptional precision across training, testing, and validation data in predicting UCSmicp, with ANN demonstrating the highest accuracy, followed by GPR, ANFIS, and GEP. Sensitivity analysis identifies CaCO3 as the main influencing factor on UCSmicp, while monotonicity analysis confirms alignment with physical processes. This work advocates for MICP as an eco-friendly solution and highlights the efficacy of advanced ML techniques in fostering sustainable infrastructure.