Artificial Intelligence Solutions Research Articles

Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.

In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensive review delves into current strategies for addressing OMBC, covering locoregional and site-specific metastasis management, and addressing both surgical and minimally invasive therapies as essential components. Moreover, the transformative role of Artificial Intelligence (AI) is spotlighted. However, while the future looks promising, several limitations need addressing, including the need for further research, especially in diverse patient populations and resource-challenged settings. AI implementation may require overcoming the lack of Electronic Health Records acceptance in resource-challenged countries, which contributes to a scarcity of large datasets for AI training. As AI continues to evolve, validation and regulatory aspects must be continually addressed for seamless integration into clinical practice. In summary, this review outlines the evolving landscape of OMBC management, emphasizing the need for comprehensive research, global collaboration, and innovative AI solutions to enhance patient care and outcomes.

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT). Summarizing recent research findings underscores the significant strides made in the accurate assessment of pCR using AI, including deep learning and radiomics. Such AI-driven models offer promise in optimizing clinical decisions, personalizing treatment strategies, and potentially reducing the burden of unnecessary treatments, thereby improving patient outcomes. Furthermore, the review acknowledges the potential of AI to address healthcare disparities in Low- and Middle-Income Countries (LMICs), where accessible and scalable AI solutions may enhance BC management. Collaboration and international efforts are essential to fully unlock the potential of AI in BC care, offering hope for a more equitable and effective approach to treatment worldwide.

Evaluating AI's Efficacy in Enhancing Patient Education and Answering FAQs in Plastic Surgery: A Focused Case Study on Breast Reconstruction

Background: The rapid advent of artificial intelligence (AI) and machine learning (ML) in the healthcare sector offers new horizons for patient education and has the potential to redefine patient-provider interactions. Our research aimed to gauge the efficacy of ChatGPT-4 in delivering accurate, current, and safe medical guidance on breast reconstruction, and benchmark its responses against other established patient information channels. Methods: We presented ChatGPT-4 with six frequently posed questions about breast reconstruction. The model's replies were critically assessed by a committee of experienced plastic and reconstructive surgeons. To ensure the precision of the information, its responses were further cross-referenced against two major medical databases. Results: The results revealed that ChatGPT-4 produced well-articulated, factually sound, and holistic answers to the presented inquiries. However, the platform showed constraints in offering tailored guidance and occasionally cited outdated or irrelevant references. Notably, the system consistently advocated for professional consultation for nuanced information. Conclusion: ChatGPT-4 has emerged as a potential supplementary resource in patient education concerning breast reconstruction. Nevertheless, to harness its full potential and ensure its seamless integration into healthcare, further refinements and advancements in AI tools are paramount. The study underscores the importance of continuous evaluation and enhancement for AI solutions in the evolving landscape of patient education.

Choosing the right artificial intelligence solutions for your radiology department: key factors to consider.

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care.

The 4S Model for AI Adoption

Overview Organizations often face challenges in exploiting the opportunities of digital technologies due to a technology-­centric focus that overlooks user value. This study explores how organizations can develop artificial intelligence (AI)-based solutions from a human-centered perspective. An in-depth case study of two projects that use both design thinking and technology development to adopt AI solutions, yielded a new hybrid approach, which we call the 4S Model. The 4S Model, which combines design thinking and technology development approaches for adopting AI-based solutions, draws on the theoretical lens of microfoundations, defining (1) the process phases (storming, solving, scoping, scaling); (2) the individuals involved (designers, technology experts, users); and (3) the structure and tools required (blueprint, pilot AI solutions, façade prototyping). The 4S Model provides guidelines for organizations seeking to apply design thinking to the deployment of AI.

Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread

The spread of infectious diseases is a highly complex spatiotemporal process, difficult to understand, predict, and effectively respond to. Machine learning and artificial intelligence (AI) have achieved impressive results in other learning and prediction tasks; however, while many AI solutions are developed for disease prediction, only a few of them are adopted by decision-makers to support policy interventions. Among several issues preventing their uptake, AI methods are known to amplify the bias in the data they are trained on. This is especially problematic for infectious disease models that typically leverage large, open, and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable policy interventions. Therefore, there is a need to gain an understanding of how the AI disease modeling pipeline can mitigate biased input data, in-processing models, and biased outputs. Specifically, our vision is to develop a large-scale micro-simulation of individuals from which human mobility, population, and disease ground truth data can be obtained. From this complete dataset – which may not reflect the real world – we can sample and inject different types of bias. By using the sampled data in which bias is known (as it is given as the simulation parameter), we can explore how existing solutions for fairness in AI can mitigate and correct these biases and investigate novel AI fairness solutions. Achieving this vision would result in improved trust in such models for informing fair and equitable policy interventions.

Role of Artificial Intelligence in Workforce Management: An Overview of its Benefits

Organisations have long relied on arduous manual processes in managing their workforce planning activities. HR professionals are under intense pressure to keep up pace with the dynamic workforce scheduling in light of the persistent labour market disruptions. Recent advancements in Artificial Intelligence (AI) have made it possible for advanced data applications that help optimize many business processes. Managements are now enabled with AI solutions that range from automation of routine tasks to assisting them create strategies for talent management and retention. This research paper analysis how AI-powered tools are transforming the HR landscape and are being increasingly utilised by business entities to optimize their workforce management functions. The indispensable role played by AI in workforce planning in the retail, healthcare and finance industries in overcoming their challenges and optimizing their performance is examined. This research study demonstrates how adoption of AI has led to increased productivity and efficiency and emphasizes the crucial role it would continue to play in the future HR workforce management functions.

A STUDY ON ARTIFICIAL INTELLIGENCE IN HR

Artificial Intelligence is rapidly revolutionizing so many industries at such an alarming rate that one such advanced AI robot, Sophia, joined the panel and was pitched questions during the United Nations’s convention on sustainable development. Artificial intelligence is producing multiple solutions for hiring managers including basic recruiting tools, intermediate applications and advanced AI solutions. Together or independently, these tools are creating a more effective way for human resources to predict a candidate’s future success with their company. artificial intelligence (AI) is transforming the human resources field altogether. The current study would throw some light on artificial intelligence breakthroughs and implications with respect to HR . KEYWORDS: Artificial, Intelligence, Human, Resources, Functions, Implications

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.

Artificial Intelligence Solution for Relaxation Oscillation Equation

Artificial Intelligence Solution for Relaxation Oscillation Equation

A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends

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.

Challenges and Opportunities of AI in Banking Sector

This systematic literature review (SLR) conducts a comprehensive analysis of the challenges and opportunities arising from the application of artificial intelligence (AI) in the banking sector, in particular Special focus on Indian banks. The review reveals many opportunities to support AI, including the rise of fintech startups providing AI solutions, regulatory support for AI integration, as well as the benefits of personalized services digitalization, smart wallets, and improved decision-making capabilities. However, these opportunities come with notable challenges, such as job mobility, privacy concerns, reduced creativity and adaptability, and gaps in abilities, digital access. The article highlights the need for banking industry stakeholders to develop effective strategies to address these challenges and align AI initiatives with overall business goals. Additionally, the study highlights the lack of empirical research on AI in banking, emphasizing the importance of future research to expand the existing knowledge base and provide insights actionable identity for industry stakeholders. This study aims to provide insights into how Indian banks can leverage AI effectively while navigating the challenges inherent in its adoption. Keywords: Artificial Intelligence, AI adoption, banking sector, challenges, opportunities, fintech, regulatory support, job displacement, privacy concerns, digital access.

A Study on AI in Marketing and Improving the Business Procedure

Everything will be accessible online in early 2020. To put it briefly, everything is bought and sold online. It represents a wise company with a wise customer. Artificial intelligence is being used in various industries, including business and engineering, because it is dependable, economical, capable of solving complex issues, and able to make expert system decisions. Perhaps the most prevalent and dynamic kind of marketing available today is social media. Artificial intelligence (AI) assists marketers in analyzing brand visibility and interactions to gauge client happiness. Consequently, a marketer can effectively promote his goods and services and run his firm thanks to the strength of artificial intelligence systems in digital marketing tactics. AI is becoming more and more sophisticated in marketing, and marketers have the ability to use it to their advantage by properly implementing and overseeing AI solutions, which will become increasingly important for enhancing the company.

REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION

Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction of energy demand. One of the key advantages of AI in energy efficiency optimization is its ability to adapt and learn from data, leading to continuous improvement in energy-saving strategies. AI algorithms can optimize energy consumption based on factors such as weather conditions, occupancy patterns, and energy prices, resulting in significant cost savings and environmental benefits. Furthermore, AI enables the integration of renewable energy sources into existing energy systems by predicting renewable energy generation and optimizing its use. This integration helps reduce reliance on fossil fuels and mitigates greenhouse gas emissions, contributing to a more sustainable energy future. However, the implementation of AI in energy efficiency optimization is not without challenges. These include data privacy concerns, the need for specialized skills to develop and deploy AI solutions, and the complexity of integrating AI systems into existing energy infrastructure. Addressing these challenges will be crucial for realizing the full potential of AI in energy efficiency optimization. In conclusion, AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems. By leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future.
 Keywords: Role, AI, Energy, Efficiency, Optimization.

"Better than my professor?" How to develop artificial intelligence tools for higher education.

Artificial Intelligence (AI) tools are currently designed and tested in many fields to improve humans' ability to make decisions. One of these fields is higher education. For example, AI-based chatbots ("conversational pedagogical agents") could engage in conversations with students in order to provide timely feedback and responses to questions while the learning process is taking place and to collect data to personalize the delivery of course materials. However, many existent tools are able to perform tasks that human professionals (educators, tutors, professors) could perform, just in a timelier manner. While discussing the possible implementation of AI-based tools in our university's educational programs, we reviewed the current literature and identified a number of capabilities that future AI solutions may feature, in order to improve higher education processes, with a focus on distance higher education. Specifically, we suggest that innovative tools could influence the methodologies by which students approach learning; facilitate connections and information attainment beyond course materials; support the communication with the professor; and, draw from motivation theories to foster learning engagement, in a personalized manner. Future research should explore high-level opportunities represented by AI for higher education, including their effects on learning outcomes and the quality of the learning experience as a whole.

Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.

Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review.

Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.

Constructing Executing and Overcoming Challenges in Distributed AI Systems: A Study of Federated Learning Framework

Federated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents a thorough investigation into federated learning architecture, covering its foundational design principles, implementation methodologies, and the significant challenges encountered in distributed AI systems. We delve into the fundamental mechanisms underpinning federated learning, elucidating its merits in diverse environments and its prospective applications across various domains. Additionally, we scrutinize the technical complexities associated with deploying federated learning systems, including considerations such as communication efficiency, model aggregation techniques, and security protocols. By amalgamating insights gleaned from recent research endeavors and practical deployments, this study furnishes valuable guidance for both researchers and practitioners aiming to harness federated learning for the development of scalable and privacy-preserving AI solutions.

Understanding the development of emerging complex intelligent systems

This paper explores the intricate emergence of complex and increasingly intelligent systems (CoIS) in the wake of new possibilities created by integrating artificial intelligence (AI) solutions, building on an analysis of the emergence of CoIS using perspectives of development and change. The findings, based on rich qualitative data collected through key informant interviews with reflective practitioners from aviation, automotive and naval system domains, indicate that firms facing the emergence of CoIS, need to build capabilities allowing several logics to co-exist in a newly evolving hybrid CoIS management logic.

Disaster impact prediction in the power grid using artificial intelligence based on Texas synthetic grid data replication

Power grids are endangered yearly by disasters that could well affect our livelihood. This paper investigates an AI (Artificial Intelligence) solution to understand better and ultimately predict disasters using the data from previously observed disasters and artificially generated data. The data is fed to the AI in the form of resiliency calculations, which are handled by another sub-program and then go through stages of linear regression models, normal curving models, and decision tree models. Afterward, the main body assigns a value percentile to each of the predictions and reaches a final prediction that is reliable, fast, and accurate. The AI then reviews its decision and checks it with the actual data to see if the forecast is accurate and refines itself according to the newly gathered data. This Algorithm has massive potential implications for decision-making while facing a disaster. It can also help test out different approaches to the problems in a safe environment before the actual procedure initiates, saving time and reducing costs. This paper is an extension to this lab’s effort to simulate a real-life disaster and grid via disaster data and multi-layer analysis to predict the steps of disaster and the grid’s responses. The raw data (generator’s generations, load’s amount, lines current, etc.) is refined through the resiliency equations introduced by this lab to be evaluated via the algorithm.