Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
REVOLUTIONIZING DRUG DELIVERY INNOVATION: LEVERAGING AI-DRIVEN CHATBOTS FOR ENHANCED EFFICIENCY
6
Zitationen
1
Autoren
2024
Jahr
Abstract
This study aims to delineate the pivotal role of ChatGPT, an Artificial intelligence-driven (AI) language model, in revolutionizing drug delivery research within the pharmaceutical sciences domain. The investigation adopted a structured approach involving systematic literature exploration across databases such as PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. A selection criterion emphasizing peer-reviewed articles, conference proceedings, patents, and seminal texts highlights the integration of AI-driven chatbots, specifically ChatGPT, into various facets of drug delivery research and development. ChatGPT exhibits multifaceted contributions to drug delivery innovation, streamlining drug formulation optimization, predictive modeling, regulatory compliance, and fostering patient-centric approaches. Real-world case studies have underscored its efficacy in expediting drug development timelines and enhancing research efficiency. This paper delves into the diverse applications of ChatGPT, showcasing its potential across drug delivery systems. It elucidates its capabilities in accelerating research phases, facilitating formulation development, predictive modeling for efficacy and safety, and simplifying regulatory compliance. This discussion outlines the transformative impact of ChatGPT in reshaping drug delivery methodologies. In conclusion, ChatGPT, an AI-driven chatbot, has emerged as a transformative tool in pharmaceutical research. Their integration expedites drug development pipelines, ensures effective drug delivery solutions, and augments healthcare advancements. Embracing AI tools such as ChatGPT has become pivotal in evolving drug delivery methodologies for global patient welfare.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.423 Zit.