How AI and ML can facilitate quality improvements in biopharma

Artificial Intelligence (AI) and Machine Learning (ML) are often confused, but they are fundamentally different. AI comprises pre-built products that use established patterns for recognition and decision-making, while ML predicts outcomes based on data. ML is a subset of AI, meaning that while all ML is AI, not all AI relies on ML.[1]

The pharmaceutical industry has found applications for both. ML’s ability to analyze vast amounts of data has greatly contributed to disease identification and diagnosis, while AI assists in drug discovery, manufacturing, productivity and efficiency, which drives faster product launches and reduces costs.[2]

AI and ML driven change will impact numerous industries. The global market for AI-based software is projected to be $126 billion by 2025, up from $10.1 billion in 2018. In addition to developing new drugs, the pharmaceutical industry must utilize AI and ML to meet greater customer expectations, research incurable diseases, and navigate more complex intellectual property rights.[3]

Despite escalating costs, the benefits of AI/ML-driven predictive analytics outweigh the expenses. By making drugs more effective earlier in the development process, success rates increase while reducing clinical trial costs. This leads to faster FDA approval and lower R&D expenses.[4]

Traditional clinical trial processes must coordinate fragmented data, manual data transcription, and integration complexities. Patient-related challenges include recruitment, monitoring, and clinical trial diversity.[5]  AI and other technologies that gather, process, and analyze data can drive more precise treatments, shifting the healthcare ecosystem towards personalization, prediction, prevention, and participation. This will significantly impact patient outcomes, especially in underserved populations.[6]

The advantages of utilizing AI and ML in clinical trials include improved data collection, digital information flow, and enhanced participant recruitment. Remote monitoring increases convenience and retention, while algorithms track and predict patient behaviors, enabling more meaningful interactions. Efficiently conducted trials reduce time and costs, as smart automation decreases rework in processing clinical trial data. AI and ML technologies also enable data reuse, eliminating the need to develop “new” databases for each trial.[7]

AI’s processing speed surpasses human capabilities. Millions of articles can be reviewed at lightning speed, identifying promising prospects earlier.[8] Academia’s utilization of AI in drug research and discovery has already increased significantly.[9]

The pharmaceutical industry has embraced innovative tools and technologies for safe and effective drug delivery. AI and ML have played pivotal roles in modernizing the industry, and a recent report highlights the importance of AI for growth, particularly in pharmaceuticals. AI/ML has emerged as a dynamic catalyst for reshaping the industry’s future.[10]

The utilization of AI and ML in drug development presents significant cost benefits. AI/ML-driven predictive analytics achieve greater precision and insights that outweigh the burden of higher initial expenses. Integration of AI/ML technologies makes drugs more effective earlier, increasing success rates and reducing clinical trial costs. By minimizing trial and error, viable solutions reach the market faster.

The speed and efficiency of AI in processing data enable faster identification of promising prospects and expedites the introduction of new drugs and treatments. The continued adoption of AI and ML will shape the future of the pharmaceutical industry, leading to enhanced efficiency, better patient care, and cost savings.