Accelerating BRICS Economic Growth: AI-Driven Data Analytics for Informed Policy and Decision Making

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

AI has the potential to transform hospital administration and has recently emerged as a solution for improving operational efficiency, enhancing decision-making processes, and optimizing patient care.This paper discusses a few of the AI applications in a hospital administration such as predictive analytics, machine learning and robotic process automation.The article also looks into some of the challenges and hurdles that hinder the mass use of AI like data privacy, integrating systems, and cost of deployment among others.Through examining case studies and literature, this study emphasises the value that AI has for healthcare organizations with discussion around potentially beneficial applications and evolution.The results highlight the need of overcoming implementing challenges to fully leverage AI's potential in healthcare systems. 1.INTRODUCTIONAs a result of increased patient numbers and resource constraints, the operational costs for maintaining quality healthcare services continue to rise.The modern hospital administrator has to cope with a delicate balance of numerous interrelated activities such as: rostering staff, managing resources, overseeing finances, and coordinating the delivery of services to patients (M.Wang & J. Preininger, 2020).These competing priorities cannot be managed by traditional administrative systems that are still largely paper-based, or at best, digitized to a certain extent, hence, much work still being done manually.The implementation of automation and AI technologies, which include machine learning, predictive analytics, and natural language processing, promise to ease the administrative burden AI algorithms can unlock new facets of work improvement throughout the operational workflows.At a minimum, the available AI technologies can perform a wide array of predictive analytics related to staff patient admission, inventory records, and even clinical decision support systems.By shifting the burden of mundane routine work to the automated systems, healthcare institutions may lower their operational costs of serving patients Varghese, Choi, Tan, & Chao, 2017). 2.LITERATURE REVIEWWith the advancement of technology, its capabilities open new opportunities, particularly in healthcare which heavily relies on accurate information.These complex areas are now able to utilize the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) booming everywhere in an attempt to further improve decision making processes within hospitals, enhance daily operations, and most importantly, provide better care for patients. Machine Learning and Predictive AnalyticsThrough Machine Learning, it is now possible to analyze historical hospital data, patient records e.g.transcripts, and even develop expectations about patients, resource management, and decision making to yield better outcomes (Liu et al., 2018).Analytics is a prospective avenue for patient movement supervision, and resource management (Varghese et al., 2017).

AI (Artificial Intelligence) has been a major factor in changing the financial landscape in recent years.  The term "Smart Finance" describes use of AI tools like RPA (robotic process automation), ML (machine learning), natural language processing (NLP) to improve financial decision-making, automate repetitive tasks, and offer more in-depth understanding of how businesses operate.  Financial institutions can improve customer service, manage risk better, streamline operations, and spur long-term development with this data-driven strategy. This chapter explores how AI is transforming finance industry, analyzing its applications, challenges, and opportunities for business growth. We also give a thorough look at future of AI in financial industry and talk about the approaches for integrating AI into financial systems. Combination of AI and finance in the digital era is changing conventional corporate environments. "Smart Finance" refers to integration of AI technologies—involving machine learning (ML), predictive analytics, and NLP—into financial operations to drive strategic decision-making, improve efficiency, and enhance business growth. This paper explores how AI-powered financial systems are transforming data into actionable insights that enable organizations to operate more intelligently and competitively. AI facilitates real-time financial data analysis, enabling companies to forecast trends, detect anomalies, manage risks, and optimize investments with higher accuracy. From automated bookkeeping and fraud detection to intelligent budgeting and customer behavior analysis, AI tools are becoming critical assets in the financial decision-making process. AI improves operational efficiency and frees up financial experts to concentrate on high-level strategic duties by automating repetitive operations and reducing human error. Additionally, platforms powered by AI are able to spot patterns in large datasets that would be very difficult to find by hand. This capability empowers businesses to personalize financial products, improve credit scoring models, and develop dynamic pricing strategies that respond to market shifts. SMEs, in particular, benefit from AI-driven financial insights that were traditionally accessible only to large corporations with extensive resources. But there are drawbacks to using AI in banking as well, such as algorithmic bias, data privacy issues, as well as requirement for qualified personnel to efficiently operate AI systems.  Notwithstanding these challenges, AI has significant long-term benefits for the financial industry, promoting a more robust, informed, and flexible corporate environment.  The objective of this essay is to present thorough examination of how AI is transforming financial operations and promoting long-term company success.  It illustrates that smart finance is not just a technical development but also a strategic necessity for progressive companies through case studies and new trends.

Identifying artificial intelligence (AI) invention: A novel AI patent dataset

This article reviews, integrates, and expands upon research initiatives that explore the development and implementation of advanced artificial intelligence (AI)–driven tools and methodologies. In exploring collaborations with leading U.S. AI technology firms, including Nvidia, Dataiku, DataRobot, and C3.ai, this study is specifically aimed at identifying, preventing, and combating human trafficking. This collaborative effort seeks to create a synergistic framework that capitalizes on the unique capabilities and resources of each firm. The overarching goal is to enhance the effectiveness of AI technologies in detecting trafficking activities, analyzing diverse data sources, and providing vital support to law enforcement and NGOs in rescue and prevention efforts. The comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing. It navigates through collaborative initiatives, presenting partnerships with prominent U.S. tech firms, accompanied by case studies that showcase successful collaborative projects. Challenges inherent in collaborative AI efforts are addressed, and the paper proposes strategic solutions to overcome these obstacles. The ethical and legal considerations section scrutinizes the implications of deploying AI in human trafficking prevention, exploring the delicate balance between innovation and safeguarding privacy and civil liberties. Furthermore, the final section anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts. Specific recommendations are offered for fortifying collaborations between AI developers and tech firms, and the paper discusses broader implications for policy, suggesting avenues for future research in the dynamic field of AI-driven human trafficking prevention. In essence, this comprehensive review not only synthesizes existing knowledge but also integrates a forward-looking research initiative, contributing significantly to the ongoing discourse on leveraging technology to combat human trafficking on a global scale.

“The rapid advancement of Artificial Intelligence (AI) has transformed the recruitment landscape, enabling more efficient, data-driven, and adaptive job search mechanisms. Traditional hiring methods often suffer from inefficiencies such as lengthy hiring cycles, unconscious bias, and poor candidate-job matching. AI-driven recruitment systems address these challenges by automating resume screening, analyzing candidate behavior, and providing personalized job recommendations. This paper explores the role of AI in recruitment, focusing on machine learning (ML), natural language processing (NLP), and deep learning techniques used to enhance candidate-job matching. AI-powered tools such as resume parsers, chatbots, and predictive analytics streamline the hiring process, reducing human effort and improving decision-making accuracy. Moreover, adaptive AI algorithms continuously learn from hiring patterns to refine recruitment strategies, increasing efficiency for both employers and job seekers. Despite these advancements, AI-based recruitment presents challenges, including algorithmic bias, ethical concerns, and data privacy issues. This paper discusses potential solutions, such as bias mitigation techniques, explainable AI (XAI), and ethical AI frameworks, to ensure fairness and transparency in hiring. Through case studies of AI-driven recruitment platforms like LinkedIn, HireVue, and IBM Watson, we analyze real-world applications and their impact on job placement success rates. The findings show that AI improves hiring efficiency but requires ethical oversight. The paper concludes by identifying future research directions in AI-powered recruitment, such as blockchain integration and AI-driven psychometric testing, to further improve hiring fairness and effectiveness." Index Terms- Artificial Intelligence, AI recruitment, job matching, machine learning, NLP, automation, bias in hiring, ethical AI.

Artificial intelligence (AI) has emerged as a transformative tool in the diagnosis and management of gastrointestinal (GI) and liver diseases. In clinical practice AI consists of overlapping technologies such as machine learning (ML), deep learning, natural language processing, computer vision, and generative AI. ML is a computer learning system that can provide insight into disease risk factors and phenotypes. Deep learning is an advanced and complex form of ML, structured with different levels of specific algorithms known as convolutional neural networks that can rapidly and accurately process unstructured, high-dimensional data, such as texts, images, and waveforms. Natural language processing is dedicated to facilitating interactions between computers and humans using natural language and helps to analyze, understand, and derive actionable information from unstructured healthcare data, including electronic health records, clinical notes, medical literature, and patient-generated content. Computer vision focuses on enabling computers to see and interpret images and videos and serves as an augmentation tool for endoscopists, improving accuracy and decreasing procedural time. Generative AI is capable of creating new forms of content by learning from a large body of data in the form of text, audio, images, or video and includes large language models. AI has been used in several GI diseases such as esophageal neoplasia, gastric cancer, Helicobacter pylori infection, gastritis, GI stromal tumors, colorectal polyps, inflammatory bowel disease, irritable bowel syndrome, GI bleeding, and pancreatobiliary diseases. The potential applications of AI in liver diseases encompass a variety of conditions such as liver masses, metabolic dysfunction-associated steatotic liver disease, viral hepatitis, cirrhosis, and liver transplantation. This review discussed the common terminologies and the current status of AI in gastroenterology and hepatology, exploring its applications and ethical issues.

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)

This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML), exploring foundational concepts, emerging trends, and diverse applications. AI and ML have rapidly evolved, becoming pivotal in numerous fields including healthcare, finance, manufacturing, and autonomous systems. The review begins by outlining key concepts, including the distinctions between AI, ML, and deep learning, and delves into various learning paradigms such as supervised, unsupervised, and reinforcement learning. It highlights significant advancements, such as neural networks, natural language processing, and generative models, emphasizing their impact on industry and research. The paper also examines current trends, including the rise of ethical AI, explain ability, and the integration of AI with Internet of Things (IoT) and edge computing, which are shaping the future landscape of AI applications. Additionally, it addresses the challenges and limitations associated with AI and ML, such as data privacy concerns, model interpretability, and the need for sustainable computing solutions. By synthesizing insights from recent literature, this review provides a holistic understanding of the AI and ML domains, offering perspectives on future directions and innovations. this review aims to provide a holistic understanding of AI and ML, offering perspectives on future directions and innovations.

Artificial intelligence (AI) has rapidly emerged as a powerful tool in the global effort to prevent and control infectious diseases. With the growing burden of pandemics, antimicrobial resistance (AMR), and travel-related outbreaks, traditional public health responses face significant challenges in speed, scale, and coordination. AI technologies—ranging from machine learning (ML) and natural language processing (NLP) to deep learning and generative models—offer new capabilities across surveillance, early outbreak detection, diagnostics, modeling, vaccine development, public communication, and resource allocation. This review synthesizes the expanding applications of AI in infectious disease prevention and control and highlights its transformative potential in improving preparedness and response. AI enables real-time integration of heterogeneous data sources, supports precision diagnostics in low-resource settings, accelerates vaccine target identification, and enhances public health communication during crises. However, its implementation is not without challenges. These include data quality and bias, privacy and security, technical limitations, transparency and trust, ethical concerns, misinformation, regulatory gaps, cost and infrastructure constraints, and system fragmentation. To maximize AI’s benefits, policy frameworks should promote equitable data access, interdisciplinary collaboration, and inclusive governance mechanisms. Technical development should also prioritize transparency, adaptability, and contextual relevance. AI has the potential to reshape global infectious disease prevention and contr

The advent of Artificial Intelligence (AI) has revolutionized various domains, and Content Management Systems (CMS) are no exception. Traditional CMS workflows, often reliant on manual processes, have struggled to keep up with the growing demands for scalability, speed, and personalization in managing digital content. AI - powered CMS address these challenges by introducing automation, enhancing efficiency, and delivering personalized user experiences. Through the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision, modern CMS have evolved into intelligent platforms capable of automating workflows, optimizing content delivery, and driving data - driven decision - making. This paper explores the transformative potential of AI in CMS, focusing on its applications in workflow automation and user experience enhancement. AI enables the automation of repetitive tasks like content tagging, categorization, and scheduling, significantly reducing manual effort and human error. At the same time, it enhances user engagement by offering personalized content recommendations, optimizing search functionalities, and dynamically adapting content to user contexts. These advancements allow businesses to manage content more effectively and cater to diverse audience needs in real time. However, the integration of AI into CMS is not without challenges. Issues such as data privacy concerns, bias in AI models, and the complexity of integrating AI into legacy systems remain significant barriers. This paper addresses these challenges and proposes actionable recommendations, including the development of explainable AI systems, robust data security frameworks, and scalable architectures that accommodate diverse business needs. As businesses increasingly adopt AI - powered CMS, their potential to transform content management is undeniable. These systems not only streamline workflows and improve operational efficiency but also empower organizations to deliver meaningful and personalized experiences to users. By addressing current challenges and advancing AI technologies, the future of content management will be defined by intelligent, adaptive, and user - centric platforms that bridge the gap between technology and human creativity. This comprehensive review highlights the opportunities and challenges of leveraging AI in CMS and underscores the importance of continued research and innovation to fully realize its potential. AI - powered CMS are not merely tools for managing content but catalysts for digital transformation, enabling organizations to thrive in an increasingly dynamic digital landscape.

Ever since the commercialization of the Internet in the '90s, technology has been evolving faster than ever with the advent of cloud computing, social media, ubiquitous mobile devices, the Internet of Things (IoT), blockchain, and more. A staggering number of three billion internet users, five billion mobile users, and six billion devices are now connected through this massive global network of networks, facilitating customer information exchange and interaction never before seen in history. Driven by recent technological advances in computing power, big data, high-speed internet connection, and easier access to models built with advanced algorithms, Artificial Intelligence (AI) is the next wave of innovation, which has already come into widespread awareness in the consumer world with the emergence of virtual assistants and chatbots (e.g., Amazon's Alexa, Apple's Siri, Google's Assistant), image recognition (e.g., Facebook Photos, Google ImageNet), personalized recommendations (e.g., Netflix, Amazon) and autonomous driving (e.g., Tesla, Google Waymo). This qualitative research study intends to learn about the impact of AI on customer relationship management (CRM), specifically in the area of customer service of problem resolution. Most prior research focuses on the AI technologies leveraged in CRM systems, such as machine learning, natural language processing, voice recognition, chatbots, data analytics, and cloud infrastructure. Few extant studies have used a qualitative research methodology to gather data from industry experts to truly understand the impact of AI technologies on customer relationship management, especially in the area of customer service and problem resolution. This study aims to fill this research gap. This research contributes to the literature on AI in the context of CRM and is of value to both academics and practitioners as it provides a detailed analysis and documentation of the impact of AI on the customer service domain.

Artificial intelligence (AI) is transforming the auditing landscape by improving accuracy and efficiency while lowering auditor risk when auditing an entity's financial statements. The study aims to comprehend how AI is used to carry out the auditing procedure. To find important areas where AI tools like robotic process automation (RPA), machine learning (ML), and natural language processing (NLP) data analytics can be used to streamline audit procedures and enable continuous auditing, a thorough review of the current literature on the subject has been conducted. When comparing the audit process in the Indian business environment to that of industrialised nations, the report identifies important issues such as a lack of technology, technical skills, data security and privacy, regulatory and legislative systems, and cultural and legal aspects. When using IA in auditing, it also identifies the ethical considerations and infrastructure needs. In order to help auditors, organisations, and regulators use AI to increase the effectiveness and transparency of the audit process, this paper offers specific recommendations for better integration of AI in the auditing process, specifically in the Indian context.

This theses explores the transformative potential of artificial intelligence (AI) in the auditing industry, focusing on its implications for audit quality, efficiency, and the evolving role of human auditors. A comprehensive review of relevant literature highlights the integration of AI-driven tools such as machine learning, deep learning, and natural language processing in audit processes, emphasizing their capabilities in enhancing risk assessment, fraud detection, and data analytics. The experimental design features an innovative approach to compare the efficacy of AI and human auditors in conducting risk analysis for three companies--Blockbuster, Enron, and Lehman Brothers--presented anonymously with pre-bankruptcy financial information. The findings reveal that while AI models provided consistent and comprehensive risk assessments, human auditors displayed a tendency towards conservative evaluations. This underlines the critical role of AI in standardizing audit processes and identifying subtle risk indicators that might be overlooked by human judgment alone. The analysis underscores the necessity of combining AI's computational strengths with human auditors' strategic insights to improve audit outcomes. Ethical, regulatory, and practical considerations surrounding the adoption of AI in auditing are also examined, advocating for robust frameworks to ensure data privacy, algorithmic transparency, and audit independence. This theses contributes to the understanding of AI's role in modern auditing, advocating for its responsible integration to enhance audit quality and reliability. As the auditing profession stands at the brink of a technological revolution, this research offers valuable insights into leveraging AI to achieve more accurate, efficient, and insightful audits.