Daily Archives: June 12, 2025

Authors: Manasa Jain

 

 

Abstract:

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Keerthi Kumar

 

 

Abstract: The convergence of Artificial Intelligence (AI) and nanomedicine has sparked a transformative wave in the biomedical and pharmaceutical industries, opening new pathways for disease diagnosis, treatment, and drug delivery at the nanoscale. As AI technologies enhance the design, functionality, and application of nanomaterials, nanomedicine ventures have become highly attractive to investors seeking long-term value and breakthrough innovations. This paper presents a comprehensive analysis of investment strategies in AI-driven nanomedicine ventures, focusing on the unique technological, financial, and regulatory dynamics of this rapidly growing domain. From venture capital and private equity to public funding and strategic partnerships, the investment landscape surrounding AI-nanomedicine is evolving, driven by innovation potential, patient demand, and the promise of market disruption. By examining investment trends, risk management techniques, key success factors, and emerging market opportunities, this paper offers a strategic framework for stakeholders aiming to capitalize on this cutting-edge intersection of technology and healthcare.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Kusum Kumari, Goutam Shaw, Debosmita Sukul, Anurima Majumdar, Antara Ghosal, Koushik Pal

Abstract: This paper discusses the advancement, challenges, and future of machine unlearning with emphasis on its significance in enhancing data privacy, security, and compliance with regulatory requirements. The review process began in 2015 and is ongoing to the current year. As privacy has become the focal point within the machine learning community, along with regulations like the General Data Protection Regulation (GDPR), machine unlearning—removing specific data from machine learning models—has attracted significant attention. The process of deleting such data is naturally timeconsuming, considering that it requires a complete retraining of the entire model; hence, traditional models have a dilemma because the process of erasing data is technically challenging and usually impractical considering the associated costs of computation. Machine unlearning enhances data privacy by facilitating selective erasure of specific data points without the need for total model retraining. It also improves model responsiveness and compliance with regulations like GDPR, hence encouraging the ethical application of artificial intelligence. The advantages of machine unlearning are enhanced data privacy, enhanced model performance, efficient utilization of resources, reduction of bias, quicker updates, and ensured compliance with ethics and laws. Through out the extensive literature survey a significant gap is observed to be that there are no reproducible, standardized procedures confirming the complete and effective elimination of data without compromising model efficiency and scalability. Areas of latent application in sectors like healthcare, finance, personalized services, and federated learning are identified, particularly in situations where unlearning is required to ensure privacy and compliance with regulations.

DOI: DOI: http://doi.org/10.5281/zenodo.15783199

 

Published by: vikaspatanker

Authors: Mamata Gowda

 

 

Abstract: Maharani’s CollegeAs Artificial Intelligence (AI) transforms the biomedical industry, regulatory bodies face the critical task of ensuring that these innovations are safe, ethical, and effective for public use. From diagnostic algorithms and AI-enhanced drug development to robotic surgeries and personalized medicine, AI technologies are redefining clinical practices and research methodologies. However, their rapid integration raises significant regulatory challenges, particularly in areas concerning data privacy, algorithmic transparency, clinical validation, and liability. This paper provides an in-depth exploration of the current regulatory landscape governing AI in biomedical applications. It analyzes the roles of major regulatory agencies such as the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and others in shaping guidelines for AI deployment. Furthermore, it highlights the complexities involved in classifying AI tools, updating compliance frameworks for adaptive algorithms, and harmonizing international standards. By dissecting case studies and emerging trends, this paper offers insights into how regulatory frameworks can evolve to balance innovation with patient safety and public trust in the age of AI-powered healthcare.

DOI: http://doi.org/

 

 

Published by: vikaspatanker