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Research Article | Volume: 1, Issue: 2 Published Date: July 25, 2025 Neural Scalpel: Generative AI and Surgical Robotics for Next-Generation Neuro-OncologyAuthor(s): Santosh Kumar* Abstract: The intersection of Generative Artificial Intelligence (Gen AI) and autonomous surgical robotics is poised to redefine the landscape of neuro-oncology. This paper introduces the "Neural Scalpel" framework?an intelligent surgical paradigm where transformer-based Gen AI models guide robotic agents to perform patient-specific brain tumor resections with sub-millimeter precision. From AI-driven tumor mapping and federated learning pipelines to intraoperative neural guidance systems, we explore the technologies enabling this new frontier. Ethical, technical, and educational implications are discussed, highlighting the need for transparent, explainable, and accessible AI-robotic integration in brain cancer care. |
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Research Article | Volume: 1, Issue: 2 Published Date: July 24, 2025 Driving Digital Transformation through Intelligent ERP: A Perspective on SAP S/4HANA, AI, and Supply Chain InnovationAuthor(s): Pavan Kumar Devarashetty* Abstract: Digital transformation has become imperative for businesses aiming to remain competitive in a rapidly evolving technological landscape. Intelligent enterprise resource planning (ERP) systems, particularly SAP S/4HANA integrated with artificial intelligence (AI), have emerged as pivotal tools enabling transformative efficiencies and innovation within supply chain operations. This paper explores SAP S/4HANA's capabilities as an intelligent ERP, emphasizing its AI-driven functionalities such as predictive analytics, robotic process automation, and real-time decision-making. Through detailed analysis and real-world case studies, the research highlights the significant impact of integrating intelligent ERP on supply chain management, demonstrating measurable improvements in efficiency, responsiveness, and strategic decision-making. |
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Research Article | Volume: 1, Issue: 2 Published Date: July 23, 2025 Federated Reinforcement Learning for Edge AI Decision-Making in 6G-Enabled V2X SystemsAuthor(s): Ronak Indrasinh Kosamia Abstract: The evolution toward sixth‑generation (6G) networks introduces transformative capabilities in intelligent transportation, particularly through ultra‑reliable, low‑latency vehicle‑to‑everything (V2X) communication. As autonomous and connected vehicles generate vast amounts of data at the edge, conventional centralized learning approaches are increasingly constrained by privacy, bandwidth, and latency limitations. In this paper, we present a federated reinforcement learning (FRL) framework that enables distributed edge agents-such as vehicles and roadside units-to collaboratively learn real‑time decision policies for navigation, collision avoidance, and traffic optimization, without sharing raw data. |
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Research Article | Volume: 1, Issue: 2 Published Date: July 23, 2025 The Generative AI Sales Paradox (GASP)-Enhancing Sales Scalability at the Cost of Human-Led Relationship Building in B2B MarketsAuthor(s): Dr. Ryosuke NAKAJIMA Abstract: This study examines a growing concern in B2B sales, which it refers to as the Generative AI Sales Paradox (GASP). Generative AI tools help sales teams move faster and reach more clients. Still, they can unintentionally weaken the kind of personal relationships that matter most in high-value, trust-based transactions. This research focuses on industries such as manufacturing, professional services, and enterprise technology, where long-term client trust is essential. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 23, 2025 Integrating AI in Mobile Applications: Security and Privacy ConsiderationsAuthor(s): Naga Satya Praveen Kumar Yadati Abstract: The rapid evolution of Artificial Intelligence (AI) technologies has revolutionized mobile applications, introducing advanced personalization, real-time assistance, and predictive capabilities. However, integrating AI into mobile platforms raises critical concerns regarding user data privacy, model security, ethical compliance, and regulatory adherence. This paper examines the architectural considerations, threat models, and mitigation strategies necessary for secure and privacy-preserving AI integration in mobile applications. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 13, 2025 Immersive Learning for Future Dentists: VR, AR, and AI IntegrationAuthor(s): Fenella Chadwick Abstract: The landscape of dental education is rapidly evolving, driven by advancements in Virtual Reality (VR), Augmented Reality (AR), and Artificial Intelligence (AI). This paper explores the transformative potential of integrating these immersive technologies to enhance the learning experience for future dentists. By moving beyond traditional didactic methods, VR offers realistic and risk-free simulations of complex procedures, allowing for repeated practice and skill refinement. AR overlays digital information onto the real-world clinical environment, providing just-in-time guidance and enhancing diagnostic capabilities. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 12, 2025 Robots in the Recliner: The Transformation of Dental Practices by 2060Author(s): Fenella Chadwick Abstract: This paper envisions the profound transformation of dental practices by the year 2060, driven by the pervasive integration of advanced robotic systems. We explore the anticipated capabilities of these ?robots in the recliner?, ranging from autonomous diagnostic support and precise treatment execution to patient comfort enhancement and administrative task automation. The discussion encompasses the potential impact on clinical workflows, the evolving roles of dental professionals, and the implications for patient access and the overall dental experience. Furthermore, the abstract touches upon the key technological advancements, ethical considerations, and economic factors that will shape the widespread adoption of robotics in the future of dental care. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 11, 2025 Smarter Smiles: The Convergence of Artificial Intelligence and Robotics in 2050 Dental CareAuthor(s): Fenella Chadwick Abstract: This paper explores the transformative potential of converging Artificial Intelligence (AI) and robotics in shaping the landscape of dental care by the year 2050. We delve into the anticipated advancements in AI-powered diagnostic tools, personalized treatment planning, and the integration of sophisticated robotic systems for precise surgical interventions and routine procedures. The confluence of these technologies promises to enhance efficiency, accuracy, and patient experience, potentially leading to earlier disease detection, minimally invasive treatments, and improved long-term oral health outcomes. This paper also considers the ethical, economic, and educational implications of this technological shift, highlighting the need for proactive adaptation within the dental profession to fully leverage the benefits of this evolving paradigm. |
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Research Article | Volume: 1, Issue: 1 Published Date: June 11, 2025 A Progressive Framework for Supply Chain Optimization: From Rule-Based Logic to Advanced Mathematical ModelsAuthor(s): Uday Dhembare and Siddharth Chaudhary Abstract: This paper presents an innovative framework for addressing complex supply chain optimization problems through a staged implementation approach, progressing from simple rule-based logic to sophisticated mathematical models. The framework emphasizes business acceptance and practical implementation while maintaining continuous improvement capabilities. Our extensive research across multiple industries demonstrates how organizations can evolve from basic Excel-based decision models to advanced mixed-integer linear programming solutions while maintaining high business acceptance rates. The study shows that this progressive approach achieves a 92% business acceptance rate while improving operational efficiency by 35% across various supply chain scenarios. |
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Research | Volume: 1, Issue: 1 Published Date: May 31, 2025 The Rise of Robotics in Healthcare: Automation and Precision MedicineAuthor(s): Soren Falkner Abstract: The healthcare landscape is undergoing a significant transformation with the increasing integration of robotics. This paper explores the burgeoning role of robotics in healthcare, highlighting its impact on both automation of routine tasks and the advancement of precision medicine. From robotic surgery and rehabilitation to automated dispensing systems and diagnostic support, we examine how these technologies are enhancing efficiency, accuracy, and patient outcomes. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 31, 2025 The AI Doctor: Artificial Intelligence in Medical Diagnosis and TreatmentAuthor(s): Soren Falkner Abstract: Artificial intelligence (AI) is rapidly transforming healthcare, particularly in the realms of medical diagnosis and treatment. This abstract explores the burgeoning role of AI algorithms and machine learning techniques in augmenting the capabilities of medical professionals. AI-powered systems are being developed and implemented for a wide range of applications, including the analysis of medical images for early disease detection, the prediction of patient outcomes and risk stratification, the personalization of treatment plans based on individual patient data, and the acceleration of drug discovery and development. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 28, 2025 Ethical Challenges of using Artificial Intelligence in Medicaid ServicesAuthor(s): Anand Laxman Mhatre Abstract: AI is one of the technologies that is quickly being incorporated into Medicaid services. Although the assimilation of AI in Medicaid services promises a plethora of benefits, these benefits will only be achievable if CMS addresses ethical issues related to the use of the technology. This paper discusses AI ethical issues in Medicaid and proposes ways these concerns can be resolved. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 26, 2025 AI-Driven Risk Stratification Models for Medicaid: Algorithms, Bias, and Validation ChallengesAuthor(s): Anand Laxman Mhatre Abstract: AI risk stratification models can play a significant role in creating patient risk scores, allowing Medicaid managers to project care delivery costs and allocate resources more efficiently. However, these models can be biased and opaque, hampering their effectiveness in risk stratification. This document explores algorithms that can be deployed in Medicaid risk stratification and their vulnerabilities. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 23, 2025 Securing AI Chatbots for Medicaid Services: Architecture, Security, and Best PracticesAuthor(s): Anand Laxman Mhatre Abstract: Although chatbots are state-of-the-art technologies that enhance user engagement and reduce operational costs, recent evidence suggests that cybercriminals are exploiting them as conduits for accessing company networks and protected data. This document discusses mechanisms and strategies that can be employed to protect Medicaid chatbots. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 22, 2025 Modernizing Medicaid IT: Cybersecurity Compliance, Cybersecurity Requirements, and Zero Trust ArchitectureAuthor(s): Anand Laxman Mhatre Abstract: The Medicaid program serves close to 90 million people and receives funding of about 3.5 percent of the GDP. To handle this large number of users and efficiently manage and administer allocated funds, Medicaid systems must be secure and compliant with healthcare data privacy regulations. This document discusses cybersecurity regulations that apply to Medicaid IT systems, and explores requirements and frameworks necessary for ensuring safety and compliance of these systems. |
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Research Article | Volume: 1, Issue: 1 Published Date: May 21, 2025 Future Trends: The Next Frontier of AI Innovations in Medicaid Health Care DeliveryAuthor(s): Anand Laxman Mhatre Abstract: Although artificial intelligence is widely used in the healthcare sector, its proliferation in Medicaid services has been limited to less complex functions despite having many use cases for advanced roles. This limited use is attributed to regulatory uncertainties, funding constraints for tech initiatives, and fragmented data in Medicaid services. The good news is that the federal government and state governments have begun addressing these barriers. This paper discusses the current state of AI in Medicaid and future innovations when the aforementioned issues are addressed. |
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Review Article | Volume: 1, Issue: 1 Published Date: May 23, 2025 Cross-Layer Coordination of Multi-Agent Systems in Internet of Streams Architectures: Enhancing Efficiency and Interoperability in IoT and Big Data FrameworksAuthor(s): Raghu K Para Abstract: The emergence of the Internet of Streams (IoS, a paradigm emphasizing the real-time generation, processing, and management of continuous, high-velocity data streams, has introduced significant challenges in scalability, interoperability, and resource optimization. These challenges are particularly pronounced in Internet of Things (IoT) and big data frameworks, where data flows span multiple layers, from physical sensing devices to application-level decision-making. Multi-Agent Systems (MAS) have emerged as a powerful framework for managing such dynamic environments, enabling distributed, autonomous, and adaptive coordination. However, the integration of MAS into IoS architectures introduces significant challenges, particularly in achieving cross-layer coordination across data, network, and application layers. |
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Research Article | Volume: 1, Issue: 1 Published Date: April 19, 2025 AI Workflow Optimization in Clinical and Regulatory EnvironmentsAuthor(s): Ramesh Pingili Abstract: In clinical and pharmacy benefit environments, automation is often hindered by regulatory constraints, policy volatility, and the need for human judgment. This article introduces a layered architecture for regulatory-grade automation, integrating AI-driven recommendations with mandatory oversight checkpoints. Drawing on a case from a U.S. health system's drug replenishment and utilization review workflow-where delays in clinical approvals resulted in 12-18% lag in patient access-the system redesign introduces structured roles for AI assistance, human intervention and traceable audit logging. Results show a 36% improvement in approval accuracy and a 22% reduction in cycle time, without sacrificing compliance. |
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Editorial Article | Volume: 1, Issue: 1 Published Date: April 18, 2025 Enhancing Metacognitive Competencies through Human-Centered AI: The Role of Custom-Trained Intelligent Agents in Workforce UpskillingAuthor(s): James Hutson Abstract: This editorial examines the integration of human-computer intelligent interaction (HCII), specifically through human-centered artificial intelligence (AI) and custom-trained intelligent agents, to foster metacognitive competencies critical for workforce upskilling. With 59% of the workforce projected to require substantial upskilling by 2030, developing personalized AI models tailored to individual cognitive and learning profiles presents an innovative pathway. |
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Research Article | Volume: 1, Issue: 1 Published Date: March 31, 2025 Client-AI Synergy: A Framework for Collaborative Inference between Edge and Cloud in Real-Time ApplicationsAuthor(s): Mariappan Ayyarrappan* Abstract: The integration of AI capabilities into edge devices has opened new frontiers for real-time applications across industries. However, the trade-offs between client-side performance and cloud-based intelligence require a hybrid approach. This paper introduces "Client-AI Synergy," a novel framework for collaborative inference that dynamically distributes machine learning tasks between client and cloud environments based on latency, computational load, and data sensitivity. The proposed system enables real-time adaptation using reinforcement learning techniques to optimize inference routing. Performance evaluations in simulated environments show significant improvements in responsiveness and resource efficienc |
Editor in Chief
Dr. James Hutson
Lead XR Disruptor and Department Head of Art History
AI, and Visual Culture
Lindenwood University, USA