The applications of AI (Machine learning & RPA) Phase III Clinical trials in India

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Chaurasia, Shubhangi
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Rapid developments in machine learning (ML) and robotic process automation (RPA), both aspects of Artificial intelligence (AI), have resulted in a major change in several sectors, including the pharmaceutical and healthcare industries. After receiving regulatory permission and being made available on the market, phase III clinical studies are essential for assessing the safety, effectiveness, and side effects of new drugs and medical treatments. To improve their efficiency and effectiveness, however, these experiments frequently need a lot of time and money and are at risk of human error.

This research makes valuable contributions by filling the highlighted research gap through a thorough examination of the viability, challenges, and benefits associated with the implementation of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) technologies in Phase III Clinical Trials. By conducting a comprehensive analysis of academic work, relevant approaches, and practical instances, this study highlights the inherent capabilities of these technologies in enhancing functional efficacy, data accuracy, and well-informed decision-making processes within the field of clinical trials.

The integration of different aspects of the research is facilitated by the identification of a research gap and a thorough examination of the existing literature. The research aims aim to evaluate the suitability of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) in Phase III Clinical Trials within the India. The research enquiries aim to explore and identify the difficulties that exist and viable solutions to address them. The research technique combines qualitative interviews with key stakeholders and quantitative analysis of historical trial data.

The findings highlight the positive effects of combining AI, ML, and RPA, focusing improved patient enrolment, improved monitoring practices, and more trustworthy data analysis. The analysis of these data situates the outcomes within the distinct framework of clinical trials conducted in India, taking into consideration the regulatory, cultural, and technological complications that are relevant to the area.

The main goal of this research is to provide detailed information on the implementation of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) in Phase III Clinical Trials conducted in India. It offers practical suggestions for key stakeholders such as pharmaceutical companies, regulatory agencies, and researchers, empowering them to effectively adopt and leverage new technological breakthroughs.

In conclusion, this study offers important new information about the possible influence of AI (ML & RPA) in Phase III clinical trials in India. Stakeholders in the pharmaceutical sector, researchers, and regulatory agencies can make well-informed judgments on the use of these technologies by considering the advantages and limitations of AI adoption. To ensure the successful incorporation of AI in the effort of increased clinical research quality in India, the study also highlights the significance of addressing ethical concerns and embracing technical breakthroughs

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