AI in Pharma

Transforming Sample and Biomarker Data into a Force Amplifier and Decision Accelerator

1. Could you provide insights into how artificial intelligence (AI) is currently being employed to analyse sample and biomarker data in clinical research?

AI is currently being employed in various ways throughout clinical research. Some examples include the identification of drug targets, patient recruitment, and the synthesis of information from scientific literature. AI and machine learning algorithms can help researchers sift through vast amounts of clinical trial data before a clinical trial begins, which can help determine the appropriate patient population for their study. One of its applications in clinical trial research and design is the amplification of data management and insight generation to support patient selection strategies, drive clinical trial efficiency, and democratise knowledge across scientific teams, which is a focus area of QuartzBio’s AI-powered Biomarker Intelligence Platform to support critical workflows and decisions that must be made throughout the precision medicine lifecycle.

2. How do these AI applications enhance the efficiency and accuracy of sample and biomarker data analysis?

One of the core challenges faced by the life science industry and more specifically by drug development teams is a disconnected data and technology ecosystem, resulting in fragmented insights from sample and biomarker data assets. Instead of spending time on high-value work, teams are left manually navigating between various files or spreadsheets to analyse this critical data. QuartzBio’s AI-enabled Data Management and Business Intelligence tools can address this challenge by streamlining and automating data ingestion, quality control, and standardisation. This process establishes a high-quality data foundation, which subsequently enables insights through conversational and prescriptive AI capabilities. These insights are easily consumable and highlight operational and scientific trends within a sponsor’s data asset.

Learn more: https://www.pharmafocusasia.com/information-technology/ai-in-pharma-transforming-sample-biomarker-data?divya