Daily Archives: May 8, 2025

Natural Language Processing in Digital Health: Transforming Clinical Narratives into Actionable Intelligence
Authors:-Vishal K

Abstract-:Natural Language Processing (NLP), a subfield of artificial intelligence, is revolutionizing digital health by converting unstructured clinical narratives into structured, actionable intelligence. With the exponential growth of electronic health records (EHRs), clinicians and researchers are confronted with vast amounts of textual data that often remain underutilized. NLP addresses this challenge by enabling automated extraction, interpretation, and analysis of clinical texts such as physician notes, discharge summaries, and pathology reports. This review explores how NLP is being leveraged across healthcare domains, from improving patient outcomes and streamlining administrative processes to supporting research and population health surveillance. It discusses key applications such as clinical decision support, disease surveillance, sentiment analysis, and adverse drug event detection. The article further examines current challenges including data privacy, accuracy of language models, and domain-specific language barriers. As the healthcare ecosystem increasingly integrates AI technologies, NLP stands out for its ability to decode human language and deliver meaningful insights from data. The future of digital health will depend heavily on the maturation of NLP tools, which can democratize access to information and personalize healthcare delivery. This review serves as a comprehensive guide to understanding the role, advancements, and implications of NLP in transforming modern clinical practice.

DOI: 10.61137/ijsret.vol.11.issue2.443

Published by: Kajal Tripathi

AI-Driven Discovery of Nanostructures That Disrupt Antibiotic-Resistant Biofilms
Authors:-Sahana M

Abstract-:Antibiotic-resistant biofilms pose a significant challenge to modern healthcare, complicating the treatment of infections associated with chronic diseases and medical devices. These biofilms provide bacteria with a protective barrier that shields them from antibiotics and immune responses, making infections difficult to treat. The development of novel therapeutic strategies to disrupt these biofilms is crucial in overcoming antibiotic resistance. Nanotechnology, particularly engineered nanostructures, holds great promise for addressing this challenge. Recent advancements in artificial intelligence (AI) have enabled the acceleration of the discovery and optimization of nanomaterials for biofilm disruption. This article explores how AI can be applied to the design, synthesis, and testing of nanostructures that target antibiotic-resistant biofilms, offering new insights into the development of more effective treatments.

DOI: 10.61137/ijsret.vol.11.issue2.442

Published by: Kajal Tripathi

Master Data Management in Healthcare: AI-Driven Architectures for Data Governance and Security
Authors:-Rishanth

Abstract-:The healthcare industry is undergoing a profound digital transformation, driven by the exponential growth of data from electronic health records (EHRs), wearable devices, genomics, and telemedicine. Amid this data surge, Master Data Management (MDM) has emerged as a critical strategy to harmonize, manage, and govern healthcare data efficiently. With the integration of Artificial Intelligence (AI), MDM systems are becoming more robust, scalable, and secure, ensuring better patient outcomes, streamlined workflows, and enhanced decision-making. This review explores the development and adoption of AI-driven architectures for MDM in healthcare, focusing on their role in ensuring data governance, integrity, security, and compliance with regulatory standards like HIPAA and GDPR. Sections examine the key challenges of traditional MDM approaches, how AI enhances data quality and governance, and the implementation of machine learning algorithms in data cleansing, deduplication, and metadata management. The article also highlights use cases, ethical implications, and future trends where AI and MDM intersect in improving healthcare systems globally. As healthcare organizations strive for digital maturity, AI-driven MDM offers a pathway toward a unified, trusted, and secure data ecosystem that is both agile and adaptive to the evolving needs of clinicians, patients, and policymakers.

DOI: 10.61137/ijsret.vol.11.issue2.441

Published by: Kajal Tripathi

The Role of Machine Learning in Transforming Financial Market Analytics and Algorithmic Trading
Authors:-Mohammed Omer

Abstract-:Machine learning (ML) has revolutionized financial market analytics and algorithmic trading by enabling data-driven decision-making, enhancing predictive accuracy, and automating complex processes. This review explores ML’s transformative role across key areas, including fraud detection, risk management, high-frequency trading, and sentiment analysis. By analyzing historical data and identifying non-linear patterns, ML models outperform traditional statistical methods, offering insights into market trends, asset pricing, and portfolio optimization. However, challenges such as data quality, model interpretability, and regulatory compliance persist. The integration of reinforcement learning, deep neural networks, and alternative data sources underscores ML’s potential to reshape financial ecosystems, though ethical considerations and systemic risks require vigilant oversight. This article synthesizes advancements, applications, and future directions, emphasizing ML’s capacity to balance innovation with stability in global markets.

DOI: 10.61137/ijsret.vol.11.issue2.440

Published by: Kajal Tripathi

AI-Assisted Design of Nanoantibiotics to Combat Multidrug-Resistant Organisms
Authors:-Kushal B

Abstract-:The rise of multidrug-resistant (MDR) organisms poses a substantial challenge to global health, severely limiting the therapeutic options available and exacerbating mortality rates. Traditional antibiotics are increasingly ineffective against these resistant pathogens due to the mechanisms they employ to neutralize or avoid the action of antibiotics. Nanoantibiotics, engineered from nanoparticles with antimicrobial properties or functionalized with antimicrobial agents, offer an innovative approach to combat these resistant microorganisms. However, the design of effective nanoantibiotics requires an in-depth understanding of the interactions between nanoparticles and microbial cells, as well as a precise optimization of the physicochemical properties of nanoparticles. Artificial intelligence (AI) has emerged as a powerful tool in accelerating and enhancing the design of nanoantibiotics by enabling predictive modeling of nanoparticle behavior and guiding the development of optimized nanomaterials. This article explores the application of AI in the design of nanoantibiotics, including the methodologies used, challenges, and future directions for AI-assisted nanomedicine in addressing MDR organisms.

DOI: 10.61137/ijsret.vol.11.issue2.439

Published by: Kajal Tripathi

Multiscale Modeling of Microbial Interactions in Nanostructured Environments
Authors:-Harish L

Abstract-:Microbial interactions with nanostructured materials are a topic of great significance in various scientific and industrial fields, particularly in medicine, biotechnology, and environmental science. Nanostructures, due to their unique properties such as small size, high surface area, and reactivity, can significantly influence microbial behavior. Understanding the complex interactions between microorganisms and nanostructured environments is essential for advancing the use of nanomaterials in drug delivery, biofilm control, and microbial bioremediation. This article explores the role of multiscale modeling in studying microbial interactions with nanostructures, emphasizing how the integration of various models at different spatial and temporal scales offers a more comprehensive understanding of these interactions and their implications for future applications.

DOI: 10.61137/ijsret.vol.11.issue2.438

Published by: Kajal Tripathi

Simulating Immune-Nano Interactions Using AI-Enhanced Agent-Based Models
Authors:-Gunashekar D

Abstract-:The interface between nanomaterials and the immune system is a critical aspect of the success of nanomedicine, particularly for applications like drug delivery and diagnostic imaging. The complexity of immune-nano interactions, which are influenced by the physicochemical properties of nanoparticles and the dynamic responses of immune cells, necessitates sophisticated modeling techniques. Agent-based models (ABMs) have been widely employed to simulate such interactions due to their ability to represent individual agents, such as immune cells and nanoparticles, and track their interactions over time. However, traditional ABMs often struggle with accurately simulating the nonlinear, high-dimensional relationships that govern these complex biological processes. By integrating artificial intelligence (AI) into ABMs, it becomes possible to enhance the predictive accuracy of these models, enabling more efficient designs for nanomedicine applications. This article explores the integration of AI with agent-based models to simulate immune-nano interactions, discussing the methodology, advantages, and challenges associated with this approach.

DOI: 10.61137/ijsret.vol.11.issue2.437

Published by: Kajal Tripathi

Machine Learning Approaches to Engineer Nanoantibiotics for Treating Infections in Immunocompromised Patients
Authors:-Bhagya S

Abstract-:Microorganisms interact with their environment across multiple scales, ranging from molecular interactions to population dynamics. In the presence of nanostructures, the complexity of these interactions increases, as nanomaterials possess the unique ability to alter microbial behavior at both the cellular and molecular levels. Their applications are vast, spanning antimicrobial coatings, medical devices, biosensors, and bioremediation technologies. Despite these advancements, the exact mechanisms by which nanostructures influence microbial behavior, including biofilm formation, antibiotic resistance, and metabolic activity, are not fully understood. The integration of multiscale modeling, which combines molecular dynamics simulations with population-level models, holds significant promise in unraveling these complexities. This paper explores the interaction of microorganisms with nanomaterials, the role of multiscale modeling, and the potential applications in healthcare, biotechnology, and environmental science.

DOI: 10.61137/ijsret.vol.11.issue2.436

Published by: Kajal Tripathi

Reinforcement Learning Applications in Autonomous Systems: From Traffic Optimization to Robotics
Authors:-Asha Devi

Abstract-:Reinforcement Learning (RL), a dynamic branch of machine learning, has emerged as a powerful tool for enabling autonomous decision-making in complex and uncertain environments. By learning through interaction, trial, and reward-based feedback, RL equips agents to optimize their actions without requiring explicit programming. This review explores the expanding role of RL across diverse autonomous systems, including traffic management, autonomous vehicles, industrial robotics, unmanned aerial vehicles (UAVs), and healthcare robotics. In traffic optimization, RL adapts to real-time flow patterns, significantly reducing congestion. For autonomous vehicles, RL facilitates safe and efficient navigation, leveraging deep learning for real-time perception and control. Industrial robotics benefit from RL by enhancing adaptability in tasks such as assembly and material handling, while UAVs gain from RL’s ability to support complex aerial maneuvers and cooperative missions. In healthcare, RL contributes to the development of intelligent surgical and rehabilitation robots that learn from both simulation and human interaction. The integration of RL with technologies like deep learning, computer vision, and sensor fusion continues to enhance autonomy across domains. While challenges such as safety, sample efficiency, and sim-to-real transfer remain, ongoing research promises scalable, robust RL solutions. This article presents a comprehensive analysis of current applications and the future trajectory of reinforcement learning in autonomous systems.

DOI: 10.61137/ijsret.vol.11.issue2.435

Published by: Kajal Tripathi

AI-Powered Control Systems for Nanobots in Microbial Infection Zones
Authors:-Amruth P

Abstract-:The use of nanobots in treating microbial infections has emerged as a promising strategy, particularly given the growing concerns about antibiotic resistance. These nanobots are small-scale machines capable of performing highly specific tasks within the human body, including pathogen detection, drug delivery, and biofilm disruption. However, to be truly effective in microbial infection zones, where conditions are often dynamic and unpredictable, nanobots require advanced control systems. Artificial intelligence (AI) has the potential to provide these control systems with the necessary intelligence to navigate these challenging environments autonomously. This article explores the integration of AI-powered control systems in nanobots for microbial infection zones, focusing on their ability to enhance precision, adaptability, and efficiency in medical applications.

DOI: 10.61137/ijsret.vol.11.issue2.434

Published by: Kajal Tripathi