Category Archives: Uncategorized

Authors: Dr. Asha Hegde

Abstract: As mental health disorders continue to rise globally, access to timely, affordable, and stigma-free care remains a significant challenge. AI-assisted chatbot therapies have emerged as innovative tools that leverage artificial intelligence, natural language processing, and psychological frameworks such as cognitive behavioral therapy (CBT) to deliver mental health support through interactive conversations. These chatbots offer 24/7 accessibility, anonymity, and scalable interventions, making them especially valuable in underserved and remote areas. This article explores the evolution, benefits, and clinical effectiveness of chatbot therapies, highlighting their ability to personalize care, monitor emotional states, and promote user engagement. It also addresses critical ethical considerations, privacy concerns, and technical limitations while examining future directions including integration with wearable technology, hybrid AI-human models, and regulatory standardization. As part of a broader digital mental health ecosystem, AI-powered chatbots present a promising complement—not replacement—to traditional therapy, offering accessible and empathetic support that can help close the global mental health treatment gap.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.133

 

 

Published by: vikaspatanker

Authors: Ramya Reddy

Abstract: AI-powered nutritional genomics represents a groundbreaking convergence of genetic science and artificial intelligence, aiming to create personalized dietary recommendations based on individual genetic profiles. By analyzing genetic variations, particularly single nucleotide polymorphisms (SNPs), and integrating data from wearable devices, microbiome analyses, and lifestyle trackers, AI can interpret complex biological patterns to optimize nutrition and health outcomes. This approach offers significant benefits, including enhanced disease prevention, improved diet adherence, and personalized health optimization. However, it also raises challenges such as data privacy, algorithmic bias, and accessibility. As the field advances, it holds the potential to revolutionize healthcare by shifting from generalized dietary guidelines to truly personalized nutrition strategies tailored to each person’s unique biology.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.132

 

Published by: vikaspatanker

Authors: Dr. Mrunal Pathak

 

 

Abstract: – The Predictive Smart Healthcare System, leveraging machine learning for early detection, individualized treatment plans, and efficient healthcare delivery by utilizing machine learning for early detection. The system evaluates patient data to anticipate health problems using algorithms like SVM and Random Forest, enabling rapid detection and individualized treatment. By automating processes like symptom analysis, appointment scheduling, and data interpretation, it reduces the workload of healthcare professionals. Using CNN and NLP, the Medical Chatbot offers immediate medical advice, improving patient engagement. The Appointment Booking module ensures effective communication by streamlining procedures using SMTP email confirmations. Intelligent, patient-centred healthcare has advanced significantly with the combination of automation and machine learning.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Dr. Rajul Misra, Mr. Saurabh Saxena, Vivek Saini, Yeshvendra Singh, Ashish Dwivedi

 

 

Abstract: This paper presents an IoT-based BLDC motor speed control and monitoring system using Arduino Uno, ESP32 WROOM, and Blynk IoT. The system allows dual control of motor speed—via a potentiometer and remotely through the Blynk app. An IR sensor module is used to measure RPM, displaying real-time speed on an LCD (16×2). The motor is powered by an 11.1V (3S) lithium phosphate battery and controlled via a Simonk 30A ESC. This setup enables precise speed regulation with IoT-based monitoring, improving automation and efficiency.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Dr. Rajul Misra, Mr. Saurabh Saxena, Vivek Saini, Yeshvendra Singh, Ashish Dwivedi

 

 

Abstract: This paper presents an IoT-based BLDC motor speed control and monitoring system using Arduino Uno, ESP32 WROOM, and Blynk IoT. The system allows dual control of motor speed—via a potentiometer and remotely through the Blynk app. An IR sensor module is used to measure RPM, displaying real-time speed on an LCD (16×2). The motor is powered by an 11.1V (3S) lithium phosphate battery and controlled via a Simonk 30A ESC. This setup enables precise speed regulation with IoT-based monitoring, improving automation and efficiency.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

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