The U.S. AI in life science market size accounted for USD 0.95 billion in 2025 and is predicted to increase from USD 1.14 billion in 2026 to approximately USD 5.91 billion by 2035, expanding at a CAGR of 20.05% from 2026 to 2035. AI-based technology speeds up drug development, enhances the precision of genomic data interpretation, and accelerates the diagnosis of disease, which drives the growth of the market.
Artificial intelligence in life sciences applies to progressive computational algorithms and machine learning technologies to analyze challenging biological information, make predictions, and automate different technologies in research and healthcare. This process is capable of processing massive amounts of data at incredible speeds, classifying patterns and insights human might miss. These technologies are being used across the spectrum of life sciences, from basic research to healthcare applications, transforming the way to understand and interact with biological technologies. AI-based applications in the life sciences are increasing the availability of biological data, technologies like sequencing to generate multi-omics information, and progressive computing power.
Recent advancements in agentic AI-driven technology compress the timeline for novel drug development from years to months by generating novel molecules and simulating interactions and behavior in the body. Agentic AI is the most recent development in this direction, providing autonomous task generation, challenging problem-solving, and real-time decision-making abilities. These technologies process heterogeneous clinical information, adapt to emerging situations, and refine their behaviour via the process of continual learning. Agentic AI has the strength to improve different healthcare functions, the way from healthcare decision support to operational management, by potentially lowering the human burden and enhancing care quality.
By component, the software segment contributed the largest U.S. AI in life science market share of 52% in 2025, as AI accelerates data analysis in the research, clinical, and healthcare workflows, permitting teams to move rapidly from data to actionable conclusions. Major life sciences software includes repetitive and data-intensive tasks. It lets teams handle huge volumes of information, operations, and content.
By technology, the machine learning segment contributed the largest market share of 34%, as ML simplifies the incorporation of challenging datasets, like genomic, proteomic, and metabolomic information, enabling inclusive modeling of biological technology. Machine learning is a powerful tool used to analyse complex biological data, predict medical outcomes, and accelerate scientific discovery.
By application, the drug discovery & development segment held a significant share of 31% in the U.S. AI in life science market, as AI-based technology has reformed drug discovery and advancement by speeding up timelines, lowering expenses, and growing success rates. AI-based technology in drug advancement involves rapid discovery, lower development costs, and enhanced prediction of drug safety and efficiency.
By deployment mode, the cloud-based segment contributed the largest market share of 56%, as the advantages of cloud solutions are flexibility, effectiveness, and strategic significance. Cloud services support organizations. Cloud offers access to the low-cost, unlimited computing power required to develop progressive technology.
By enterprise size, the large enterprises segment captured the major share of 68% in 2025. This dominance was driven by higher investments in AI infrastructure, strong digital transformation capabilities, extensive clinical research activities, and increased adoption of advanced analytics tools for drug discovery, diagnostics, regulatory compliance, and operational efficiency improvements across the life sciences sector.
By end-user, the pharmaceutical companies dominated the U.S. AI in life science market with a share of 36% in 2025. The segment’s growth was supported by rising use of AI for precision medicine, accelerated drug development, clinical trial optimization, biomarker identification, and predictive analytics, enabling pharmaceutical firms to improve research productivity, reduce development timelines, and enhance patient treatment outcomes.
Recursion Pharmaceuticals, this organization, uses AI and automated labs to map cellular biology and detect advanced medicine targets for rare diseases and cancer. Absci integrates generative AI-driven technology with scalable wet lab processes to design better biologic medicines. Generate Biomedicines employs generative AI foundation representations to program and create novel therapeutic proteins. Tempus AI its huge med tech company that leverages AI and machine learning to analyze molecular and healthcare data.
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