By: Roseline Tio and Izwan Firdaus (SEA CSV SME)

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Data Processing

Algorithms built to read, categorize, and analyze massive amounts of textual data are among the most advanced applications of AI to date. Researchers in the life sciences sector can save significant amounts of time by using this method, which provides a more effective way to study the vast amounts of data presented in the ever-increasing number of research articles.

In addition, paper diaries are still widely used in clinical studies, with patients recording information such as when they took a drug, what else they were taking, and whether or not they experienced any adverse side effects. Artificial intelligence capable for collecting and analyze data from various sources, including handwritten notes, test results, environmental factors, and imaging scans. Faster research, cross-referencing, data combination, and data extraction into accessible formats for analysis are just some of the benefits of using AI in this fashion. This is supported by the research conducted by Cognizant, whereby 80% of clinical trials are unable to enroll patients by their target dates, and 30% of Phase III clinical studies are halted due to recruitment issues.

In conclusion, AI is an effective tool for data mining based on huge pharmacological data and machine learning processes. AI has the potential to revolutionize pharmaceutical manufacturing by integrating the component of digitalization in CSV by providing real-time data analysis, remote monitoring, decision-support making, process optimization, quality control, and supply chain management. AI presents an ocean of untapped opportunities for business transformation. Big Data, along with AI-powered analytics, has brought about a radical shift in the innovation paradigm of the pharmaceutical sector. It is undeniable that AI will be the next big thing in the Pharmaceutical Industry, and those companies that adapt and adopt new processes will be at a competitive advantage.

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