Daily Archives: May 31, 2025

Authors: Nagendra Kumar, Manjesh Gowda

Abstract: Clinical trials are fundamental to the development of new drugs and therapies, but they are also notoriously time-consuming, expensive, and complex. With traditional processes often taking more than a decade and costing billions, there is a growing need for innovation to make clinical trials more efficient and cost-effective. Artificial Intelligence (AI) offers transformative solutions by automating data analysis, optimizing patient recruitment, improving trial design, and enabling real-time monitoring. This paper explores how AI is revolutionizing clinical trial processes, significantly reducing time and cost while improving accuracy and patient outcomes. It also examines challenges in implementation, regulatory concerns, and future prospects. By integrating AI into the clinical trial lifecycle, pharmaceutical companies, contract research organizations (CROs), and healthcare providers can accelerate drug development and deliver safer, more effective therapies to market.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.568

 

 

Published by: vikaspatanker

Authors: Shailesh Yadav

Abstract: Personalized medicine, which tailors medical treatment to individual patient characteristics, has been significantly enhanced by advances in artificial intelligence (AI). AI enables the integration and analysis of vast amounts of patient data, facilitating precise diagnostics and personalized therapeutic interventions. The adoption of AI in personalized medicine is reshaping traditional healthcare business models by introducing new value creation mechanisms, revenue streams, and stakeholder dynamics. This paper explores the evolving business models that support AI-enabled personalized medicine, focusing on value propositions, revenue generation, partnerships, and challenges in commercialization. The analysis highlights how innovative business frameworks are essential to translating AI technologies into sustainable healthcare solutions that improve patient outcomes and deliver economic value. Strategic implications for startups, established healthcare providers, and payers are discussed, alongside considerations for regulatory environments and ethical dimensions. The paper concludes by outlining future trends and opportunities for business innovation in AI-driven personalized healthcare.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.567

 

 

Published by: vikaspatanker

Authors: Sumanth Sai Krishna

Abstract: Artificial intelligence (AI) has revolutionized healthcare diagnostics by enabling faster, more accurate, and often less invasive disease detection. As AI-driven diagnostic tools become increasingly prevalent, assessing their economic impact is essential for healthcare providers, payers, and policymakers. This paper provides a comprehensive economic evaluation of AI diagnostic technologies, focusing on cost-effectiveness, budget impact, and value-based healthcare implications. It examines how AI tools influence healthcare costs, patient outcomes, workflow efficiencies, and access to care. Methodological approaches for economic evaluations, challenges in data collection and analysis, and case studies of successful AI diagnostic implementations are discussed. The paper also explores the broader systemic effects of AI diagnostics on healthcare delivery models, reimbursement strategies, and long-term sustainability. Ultimately, this evaluation underscores the potential for AI-driven diagnostics to deliver economic value while improving clinical outcomes and patient experiences.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.566

 

 

Published by: vikaspatanker

Authors: Naresh Kumar

Abstract: Artificial intelligence (AI) is reshaping drug development pipelines across the pharmaceutical industry, driving innovation, reducing costs, and shortening time-to-market for new therapies. This paper analyzes the impact of AI from a business perspective, focusing on how pharmaceutical companies and biotech startups leverage AI technologies to optimize discovery, preclinical research, clinical trials, and regulatory processes. The integration of AI not only enhances scientific outcomes but also transforms business models, investment strategies, and competitive dynamics. Challenges such as data governance, regulatory compliance, and workforce adaptation are discussed alongside strategic recommendations for successful AI adoption. This comprehensive analysis highlights how AI-enabled drug development can provide sustainable business value, foster industry disruption, and ultimately improve patient care worldwide.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.565

 

 

Published by: vikaspatanker

Authors: Professor Amit Thakur, Tanishka Mangal

Abstract: Clustering in wireless sensor network (WSN) is an efficient approach to provide prolonged network life time, scalability and data aggregation. Clustering also conserves the limited energy resources, for this reason in this work; we propose an energy aware static clustering routing protocol for WSN. The specificity of this work is that the network is partitioned into static clusters that contain a Primary Cluster Head (P-CH) and a Secondary Cluster Head (S-CH) and both of them are selected based on energy. The simulation results show that the new protocol proposed in this work extends the network lifetime and balances the energy consumption of the network nodes.

Published by: vikaspatanker

Authors: Hemanth Kumar, Madhu Gowda

Abstract: Artificial intelligence (AI) is rapidly transforming the biotechnology sector by enabling startups to accelerate research and development, optimize clinical trials, and develop personalized medicine approaches. This paper explores the strategic implementation of AI in biotech startups, examining both the remarkable opportunities AI offers and the significant challenges these emerging companies face in adopting such advanced technologies. We discuss the role of AI in drug discovery, diagnostics, and therapeutic innovation, while highlighting barriers related to data management, regulatory compliance, funding, and talent acquisition. The paper concludes by providing insights into overcoming these challenges through interdisciplinary collaboration, ethical practices, and strategic partnerships. Ultimately, successful AI integration is poised to revolutionize healthcare by enabling biotech startups to deliver groundbreaking treatments and improve patient outcomes.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.564

 

 

Published by: vikaspatanker

Authors: Selva Murugan

Abstract: Chronic diseases such as diabetes, cardiovascular disorders, cancer, and neurodegenerative conditions represent a major global health burden. Early diagnosis and personalized treatment strategies significantly improve patient outcomes, and the identification of reliable biomarkers is central to these efforts. Machine learning (ML), a subset of artificial intelligence, has emerged as a powerful tool to analyze complex biomedical data and discover novel biomarkers that traditional statistical methods may overlook. This paper explores the application of machine learning techniques in identifying novel biomarkers for chronic diseases by integrating multi-omics data, clinical records, and imaging datasets. It discusses various ML algorithms, challenges in data preprocessing and interpretation, and the translational potential of ML-driven biomarker discovery for precision medicine.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.563

 

 

Published by: vikaspatanker

Authors: Shalini Bhandar

Abstract: The traditional process of vaccine development is often lengthy, costly, and complex, involving multiple stages from antigen discovery to clinical trials. The integration of artificial intelligence (AI) in vaccine research has the potential to revolutionize this field by accelerating the design, testing, and production of vaccines. AI-powered tools and machine learning algorithms facilitate rapid antigen identification, prediction of immune responses, optimization of vaccine candidates, and streamlined clinical trial management. This paper explores how AI is transforming vaccine development by reducing timelines, enhancing precision, and improving safety and efficacy. Challenges such as data availability, model reliability, and ethical considerations are discussed, alongside future perspectives on AI-driven vaccine innovation, especially highlighted by the COVID-19 pandemic.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.562

 

 

Published by: vikaspatanker

Authors: Shruthi Singh

Abstract: Electronic Health Records (EHRs) have revolutionized healthcare by digitizing patient information, enabling comprehensive data capture across clinical settings. The integration of machine learning (ML) techniques with EHR data holds immense potential for predictive healthcare, facilitating early diagnosis, risk stratification, personalized treatment, and improved patient outcomes. This paper explores how machine learning algorithms applied to EHR datasets can transform healthcare delivery by enabling predictive analytics, clinical decision support, and population health management. Key challenges such as data quality, interoperability, privacy, and model interpretability are discussed alongside emerging solutions. The future of predictive healthcare lies in harnessing the synergy of EHRs and AI to advance precision medicine, reduce costs, and enhance healthcare accessibility.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.561

 

 

Published by: vikaspatanker

Authors: Nisha Prabhakar

Abstract: The human microbiome, consisting of trillions of microorganisms inhabiting various body sites, plays a critical role in health and disease. Recent advances in high-throughput sequencing and metagenomics have generated vast datasets characterizing the complex microbial communities and their functional capabilities. However, the intricate interactions between microbiota, host physiology, and environmental factors pose significant challenges to data interpretation and the extraction of actionable insights. Artificial intelligence (AI), particularly machine learning, offers powerful computational tools to analyze complex, high-dimensional microbiome data, identify novel patterns, predict disease associations, and inform personalized therapeutic strategies. This paper explores AI-driven approaches to deciphering the human microbiome, including data integration techniques, predictive modeling, challenges in microbiome research, and future perspectives for leveraging AI to transform microbiome science and precision medicine.

DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.560

 

 

Published by: vikaspatanker