AI In Life Sciences Market Size

AI in Life Sciences Market Analysis

The AI In Life Sciences Market size is estimated at USD 2.88 billion in 2024, and is expected to reach USD 8.88 billion by 2029, growing at a CAGR of 25.23% during the forecast period (2024-2029).

The COVID-19 outbreak has made profound and lasting impacts on the health and life sciences industry. The outbreak has caused life sciences organizations to adjust to supply chain and clinical development disruptions and financial challenges that would have previously been unthinkable. In the near term, healthcare organizations will accelerate innovation to respond to the crisis. These investments would enable healthcare organizations post-COVID-19 to rethink care delivery and financing, thereby stimulating the growth in the adoption of AI for the life sciences industry.

• Further, the industry is witnessing a transformation owing to increasing cost pressure, a greater need for productivity, and disruption caused by new and innovative market players. An emerging area of artificial intelligence (AI), specifically the analysis of small systems-of-interest-specific datasets, can be utilized to improve drug development and personalized medicine.
• According to a study published in Science Translational Medicine, Quadratic Phenotypic Optimization Platform (QPOP), an AI platform, can substantially improve combination therapy in bortezomib-resistant multiple myeloma, which is used to identify the best drug combinations for individual multiple myeloma patients.
• Furthermore, complex diseases, such as cancer, often require effective drug combinations to make any significant therapeutic impact. As the drugs in these combination therapies become increasingly specific to molecular targets, designing effective drug combinations and choosing the right drug combination for the right patient becomes more difficult. With the high average development costs (around USD 2 billion for a newly approved treatment), low clinical trial drug success rate (below 12%), low return-on-investment (ROI) due to reduced healthcare expenditure, and focus on rare diseases, drug discovery is becoming more inefficient.
• Clinical trial research is extensive progress, which can be reduced with the help of AI in numerous ways. One is using advanced predictive analytics on a broad range of data to quickly identify candidates for clinical trials for target populations. Additionally, machine learning applications can make clinical trials more efficient by facilitating tasks such as calculating ideal sample sizes, facilitating patient recruitment, and using medical records to minimize data errors.
• Artificial Intelligence (AI) presents one of the most promising and potentially transformative opportunities for the life sciences industry. AI will be a key investment target in the coming years, with myriad organizations hoping to capitalize on its potential. The number of applications is expected to continue to increase, and investors are expected to enter the AI industry early.