Authors: Vinay R. Gowda

Abstract: Artificial Intelligence (AI) is revolutionizing personalized physical therapy by enabling objective assessment, tailored treatment planning, real-time monitoring, and remote rehabilitation. By integrating data from wearable sensors, computer vision, and electronic health records, AI supports individualized care that improves patient outcomes and engagement. This article reviews AI’s multifaceted role in physical therapy, highlighting current applications in assessment, therapy customization, and tele-rehabilitation. It also addresses challenges related to data privacy, ethical considerations, and clinical integration. Emerging trends such as augmented reality, predictive analytics, and digital twin technology are discussed, outlining the future direction of AI-driven rehabilitation. The integration of AI promises to transform physical therapy from a generalized approach to a dynamic, personalized, and proactive discipline, ultimately enhancing recovery and quality of life for diverse patient populations.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.131

 

 

Published by: vikaspatanker

Authors: Dr. Vignesh Sai

Abstract: Autoimmune diseases are complex, chronic conditions characterized by immune system dysregulation and unpredictable disease progression, posing significant challenges for early diagnosis, continuous monitoring, and personalized treatment. Traditional clinical approaches often fall short in managing these multifaceted disorders effectively. This article explores the transformative role of artificial intelligence (AI) in addressing these challenges by leveraging advanced machine learning, natural language processing, and wearable technologies to improve early detection, real-time disease activity monitoring, and tailored therapeutic strategies. We discuss current applications, data and ethical considerations, and future innovations such as multimodal AI systems and federated learning, emphasizing the potential of AI to enhance patient outcomes and revolutionize autoimmune disease care. Overcoming hurdles related to data quality, privacy, and bias remains essential to fully realizing AI’s benefits. This synthesis highlights AI’s promise in enabling a more precise, proactive, and patient-centered approach to autoimmune disease management.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.130

 

 

Published by: vikaspatanker

Authors: Dr. Uma Devi T

Abstract: Accurate diagnosis in dermatology is essential for effective treatment and improved patient outcomes, yet it remains challenging due to the complexity and variability of skin conditions. Artificial Intelligence (AI), especially machine learning and deep learning techniques, has emerged as a promising tool to enhance diagnostic accuracy by analyzing vast and diverse dermatologic image datasets. AI-powered diagnostic systems can detect subtle features in skin lesions, enabling early identification of malignant and benign conditions with accuracy comparable to expert dermatologists. These technologies offer benefits such as reducing diagnostic variability, expanding access through teledermatology, and supporting clinicians in decision-making. However, challenges including data bias, model interpretability, ethical concerns, and integration into clinical workflows must be addressed for effective adoption. Future innovations involving multimodal data integration, personalized diagnostics, and explainable AI promise to further advance dermatologic care. Overall, AI has the potential to revolutionize dermatology by improving diagnostic precision, accessibility, and patient outcomes.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.129

 

 

Published by: vikaspatanker

Authors: Dr. Ashwin Kumar

Abstract: Artificial Intelligence (AI) is revolutionizing clinical trial participant recruitment by automating and optimizing key processes such as eligibility screening, patient matching, and engagement. Traditional recruitment methods face challenges including time-consuming manual efforts, low enrollment rates, and demographic disparities, which delay trials and increase costs. AI technologies—such as machine learning, natural language processing, predictive analytics, and chatbots—enable efficient analysis of complex patient data from electronic health records and other sources, improving recruitment speed, accuracy, and inclusivity. While AI-driven recruitment offers significant benefits like reduced timelines, enhanced patient retention, and cost savings, it also raises ethical and regulatory concerns including data privacy, algorithmic bias, and transparency. Future developments integrating real-world data, explainable AI, and digital health platforms promise to further advance recruitment practices. This article reviews the current applications, benefits, challenges, and future directions of AI in clinical trial participant recruitment, highlighting its potential to transform clinical research and accelerate medical innovation.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.128

 

 

Published by: vikaspatanker

Authors: Dr. Harika Prasad

Abstract: AI technologies are transforming medication adherence by enabling personalized, real-time interventions that address the complex factors influencing patients’ ability to follow prescribed regimens. By leveraging machine learning, predictive modeling, natural language processing, and data integration from diverse sources—including electronic health records, wearable devices, and patient-reported outcomes—AI systems can monitor adherence patterns, predict patients at risk of non-compliance, and deliver tailored reminders and support through virtual health coaches and chatbots. These innovations improve patient engagement, facilitate early intervention, and empower healthcare providers with actionable insights, ultimately enhancing treatment outcomes and reducing healthcare costs. However, successful implementation requires careful consideration of ethical, privacy, and regulatory challenges to ensure fairness, transparency, and patient trust. As AI continues to evolve, its integration into medication adherence management promises to revolutionize personalized care, offering scalable solutions that improve quality of life for millions worldwide.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.127

 

 

Published by: vikaspatanker

Authors: Vishal Garad, Dr. Quazi khabeer, Dr. A. A. Khan, Dr. R. S. Deshpande

 

 

Abstract: Cloud computing has transformed the business and industry operations by providing scalable, flexible, and cost- efficient data storage, processing, and access solutions.The paper discusses the revolutionary effects of cloud computing on business processes and its benefits, challenges, and future trends. It identifies the contributions of cloud technologies to enhanced operational efficiency, innovation, and sustainable practices.Index Terms—Cloud Computing, Scalability, Operational Efficiency, Security, Cost-Effectiveness, Future Trends.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Assistant Professor DR. Ritu Jain, Aditya Pratap Saroj

Abstract: The Himalayan glaciers, also referred to as the "Third Pole," play a vital role in the ecological balance of the region and supply water to more than a billion Asians, sustaining agriculture, drinking water, and energy. Climate change has resulted in rapid glacial retreat, which has resulted in decreased water resources, enhanced disaster risks, and ecosystem threats. This chapter examines the effect of glacial melt, employing remote sensing, satellite imagery, and climate models to evaluate the scale of retreat and its consequences, including water scarcity and ecosystem disruption. It discusses adaptive measures, such as enhanced water storage, flood early warning systems, and sustainable agriculture to reduce the impacts of declining glacial melt. Community-based techniques, combining indigenous knowledge, are also mentioned as important in controlling water resources. Lastly, the chapter discusses the necessity of cross-border policy and cooperation to develop solutions for the water crisis to ensure the sustainable management of the region's glaciers and water.

 

 

Published by: vikaspatanker

Authors: Assistant Professor Dr. Ritu Jain, Kajal Pandey

Abstract: Urban sprawl, defined as the haphazard expansion of city boundaries into rural and peri-urban lands, is a defining characteristic of contemporary urban growth in India. As a prominent Tier-2 city and capital of Uttar Pradesh, Lucknow has witnessed accelerated sprawl patterns over the past three decades. This paper investigates the extent, causes, and implications of this urban expansion using remote sensing data, GIS techniques, demographic trends, and field-based insights. With a focus on infrastructure strain, socio-economic disparities, environmental degradation, and spatial policy inefficiencies, this research identifies key trends, challenges, and solutions. Recommendations are proposed for integrated planning, sustainable growth, and data-driven governance.

 

 

Published by: vikaspatanker

Authors: Pallavi Srivastava

Abstract: In low-resource environments, diagnostic tools must prioritize affordability while also delivering accuracy, reliability, and durability suited to the unique challenges of the developing world. In recent years, global health diagnostics using minimally instrumented, microfluidic platforms with low-cost disposable components have gained momentum, driven in part by funding from organizations like the Bill & Melinda Gates Foundation and the National Institutes of Health. This surge in interest has resulted in a variety of promising prototype devices, many of which are undergoing advanced development or clinical testing. These include systems capable of multiplexed PCR assays targeting enteric, febrile, and reproductive tract infections, as well as immunoassays for conditions like malaria, HIV, and sexually transmitted infections. More recent innovations feature fully disposable diagnostics that operate without instruments, utilizing isothermal nucleic acid amplification techniques. Despite these advancements, scalable and truly low-cost manufacturing methods remain a major hurdle in creating affordable diagnostic solutions at volume. This overview highlights current platform development efforts, includes original research conducted at PATH, and emphasizes the need for continued action and innovation in this field.

 

 

 

Published by: vikaspatanker

Authors: Assistant Professor Dr. Ritu Jain, Himanshu Kasaudhan

Abstract: The Northeastern region of India, comprising the states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura, represents a critical ecological zone within the Indo-Burma biodiversity hotspot. This area is home to a vast range of endemic and threatened species and features one of the highest levels of biological diversity in India. However, rapid deforestation caused by anthropogenic pressures—such as shifting cultivation (jhum), illegal logging, infrastructure expansion, and population growth—has led to severe ecological degradation. This chapter explores how Remote Sensing (RS) and Geographic Information Systems (GIS) have been employed to monitor forest cover changes and assess their impact on biodiversity. Through the use of satellite imagery, spatial analysis, biodiversity indices, and ecological modeling, this study highlights the extent, patterns, and consequences of deforestation on flora and fauna. The chapter concludes by offering conservation strategies and policy recommendations grounded in geospatial data and ecological science.

 

 

 

Published by: vikaspatanker