The US AI in Life Science Market was valued at $9477.4 Million in 2026 and projected to reach to $29746.2 Million by 2031, representing a compound annual growth rate of 25.7%. The US AI in Life Science Market is poised for exceptional growth through 2031, driven by substantial investments from pharmaceutical giants, biotech startups, and healthcare institutions seeking competitive advantages in drug development and patient care.
| Market Size in | USD 26.32 MN |
| Market Forecast in | |
| CAGR | |
| Forecast Period | |
| Units Considered | Value (USD MN) |
The US AI in Life Science Market is valued at $9,477.4 million in 2026 and is projected to reach $29,746.2 million by 2031, representing a 214% increase over the five-year forecast period.
The US market is growing at a 25.7% CAGR from 2026 to 2031, outpacing many other healthcare technology segments and reflecting strong investor confidence and adoption rates across the life sciences sector.
US pharmaceutical and biotech companies are leveraging AI for accelerated drug discovery, clinical trial optimization, and patient recruitment, positioning the country as a global innovation hub in AI-driven life sciences.
American healthcare providers are increasingly implementing AI-powered personalized medicine solutions, enabling precision diagnostics and tailored treatment plans that improve patient outcomes and reduce healthcare costs.
| Report Metric | Details |
|---|---|
| Base Year | 2026 |
| Fastest Growing Segment | NATURAL LANGUAGE PROCESSING (Tool) |
| Forecast Period | 2026–2031 |
| Growth Rate | CAGR of 26.3% from 2026 to 2031 |
| Largest Segment | CLINICAL APPLICATIONS (Application) |
| Market Size Base Year (Billions) | ~USD 21.58 (2026) |
| Revenue Forecast (Billions) | ~USD 69.34 (2031) |
| Segments Covered | Tool, Type, Application, Component, Deployment, End User, Offering, Deployment Mode |
8 segment dimensions are covered across the global market.
| Segment | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 | CAGR (%) |
|---|---|---|---|---|---|---|---|---|---|
| ACADEMIC & GOVERNMENT LABORATORIES | 406 | 497.7 | 622.4 | 782.7 | 972.9 | 1220.8 | 1522.2 | 1876.5 | 24.7 |
| BIOTECHNOLOGY COMPANIES | 1480.4 | 1838.8 | 2330 | 2968.9 | 3739.2 | 4754 | 6006.1 | 7502.3 | 26.3 |
| CRO & CDMO | 1044.8 | 1283.2 | 1607.9 | 2025.9 | 2522.9 | 3171.8 | 3962.3 | 4894.1 | 24.9 |
| DIAGNOSTIC COMPANIES | 618.5 | 765.3 | 966 | 1226.1 | 1538.2 | 1948.1 | 2451.6 | 3050.4 | 25.9 |
| OTHER END USERS | 127.8 | 153.4 | 187.3 | 229.6 | 277.6 | 338 | 407.8 | 485.1 | 21 |
| PHARMACEUTICAL COMPANIES | 2405.7 | 2979.2 | 3763.8 | 4781.4 | 6003.9 | 7610.3 | 9585.7 | 11937.7 | 26 |
| TOTAL | 6083.2 | 7517.5 | 9477.4 | 12014.6 | 15054.7 | 19043 | 23935.7 | 29746.2 | 25.7 |
| Company | HQ | Ownership | Strongest segments |
|---|---|---|---|
| NVIDIA CORPORATION | United States | Public Company | Data Center AI & Accelerated Computing (Compute & Networking),Gaming GPUs (GeForce),Professional Visualization (RTX/Quadro), |
| ILLUMINA, INC. | United States | Public Company | Sequencing instruments,Sequencing consumables (reagents, flow cells, library prep, WGS and targeted kits),Array-based instruments and consumables, |
| TEMPUS AI, INC. | United States | Public Company | Oncology NGS diagnostics (xT, xR, large-panel solid and hematologic assays),Other clinical diagnostics (PCR, pathology, germline, pharmacogenomics nP),Life sciences data, AI platforms, and software (Lens, Insights, Next, Algos, Organoids, Trials), |
| RECURSION | United States | Public Company | Platform and collaboration revenue,Internal pipeline and other revenue, |
| DASSAULT SYSTèMES SE | France | Public Company | 3D Modeling (CATIA, SOLIDWORKS, GEOVIA, BIOVIA),PLM & Collaboration (ENOVIA, CENTRIC PLM, 3DEXCITE, 3DEXPERIENCE collaboration),Simulation & Manufacturing (SIMULIA, DELMIA, 3DVIA), |
| SCHRÖDINGER, INC. | United States | Public Company | Software,Drug Discovery – Collaborations and Milestones,Drug Discovery – Internal Programs and Other, |
| MICROSOFT CORPORATION | United States | Public Company | Productivity and Business Processes,Intelligent Cloud,Personal Computing, |
| BENEVOLENTAI LIMITED | United Kingdom | Private Company | AI R&D decision-support platform (licenses and subscriptions),Collaborative discovery projects and services,Data and ontology integration / customization, |
NVIDIA Corporation is a U.S. public company founded in 1993 that employs 42,000 people. The company designs and manufactures graphics processing units and system-on-chip units for gaming, professional visualization, data centers, and artificial intelligence applications.
Illumina, Inc. is a U.S. public company founded in 1998 with 8,625 employees. The company develops and manufactures sequencing and array-based solutions for genetic analysis and molecular diagnostics.
Tempus AI, Inc. is a U.S. public company founded in 2015 with 3,800 employees. The company applies artificial intelligence and machine learning to healthcare data to improve clinical decision-making and patient outcomes.
Recursion is a U.S. public company founded in 2013 with 600 employees. The company uses artificial intelligence and high-throughput biology to discover and develop novel therapeutics.
Dassault Systèmes SE is a French public company founded in 1981 with 25,000 employees. The company develops and markets 3D design, digital mock-up, and product lifecycle management software solutions.
Schrödinger, Inc. is a U.S. public company founded in 1990 with 850 employees. The company provides computational chemistry and molecular simulation software platforms for drug discovery and materials science.
Microsoft Corporation is a U.S. public company founded in 1975 with 228,000 employees. The company develops, manufactures, and sells computer software, consumer electronics, personal computers, and cloud computing services.
BenevolentAI Limited is a United Kingdom private company founded in 2013 with 173 employees. The company uses artificial intelligence to accelerate drug discovery and development for various therapeutic areas.
The US AI in Life Science Market is valued at $9,477.4 million in 2026 and is projected to reach $29,746.2 million by 2031.
The US AI in Life Science Market is growing at a compound annual growth rate (CAGR) of 25.7% from 2026 to 2031.
Key drivers include accelerating digital transformation in life sciences, regulatory support for AI-driven therapeutics, rising demand for precision medicine, and substantial venture capital investment in US-based AI life science startups.
The US leads due to robust R&D investments, advanced computational infrastructure, concentration of biotechnology and pharmaceutical companies, supportive regulatory environment, and a thriving venture capital ecosystem.
The US AI in Life Science Market is expected to more than triple in value, growing from $9.48B in 2026 to $29.75B in 2031, representing a $20.27B increase over five years.
The study involved five major activities to estimate the current size of the AI in life science market. Exhaustive secondary research was conducted to collect information on the market and its subsegments. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain through primary research. Both top-down and bottom-up approaches were employed to estimate the total market size. Thereafter, market breakdown and data triangulation procedures were used to estimate the market size of the segments and subsegments.
In the secondary research process, various secondary sources, including annual reports, press releases and investor presentations from companies, white papers, certified publications, articles by recognized authors, gold- and silver-standard websites, regulatory bodies, and databases (such as D&B Hoovers, Bloomberg Business, and Factiva), were consulted to identify and collect information for the study of the AI in the life science market. These sources were also used to obtain important information about the top players, market classification and segmentation by industry trends down to the bottom-most level, geographic markets, and key developments related to the market. A database of key industry leaders was also prepared using secondary research.
Extensive primary research was conducted after obtaining basic information of the global AI in life science market through secondary research. Several primary interviews were conducted with market experts from both the demand side (hospital directors, hospital vice presidents, department heads, and critical care specialists) and the supply side (such as C- and D-level executives, technology experts, product managers, marketing and sales managers, among others) across five major regions, including North America, Europe, the Asia-Pacific, Latin America, the Middle East, and Africa. This primary data was collected through questionnaires, emails, online surveys, personal interviews, and telephone interviews.
The following is a breakdown of the primary respondents:
BREAKDOWN OF PRIMARY PARTICIPANTS:

Note 1: Others include sales managers, marketing managers, and product managers.
Note 2: Tiers of companies are defined on the basis of their total revenues in 2025. Tier 1 = >USD 1 billion, Tier 2 = USD 500 million to USD 1 billion, and Tier 3 = <USD 500 million.
To know about the assumptions considered for the study, download the pdf brochure
Both top-down and bottom-up approaches were used to estimate and validate the total size of the AI in life science market. These methods were also used extensively to estimate the size of various subsegments in the market.

After determining the overall market size using market size estimation processes, the market was segmented into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics for each market segment and subsegment, data triangulation and market breakdown procedures were employed wherever applicable. The data was triangulated by analyzing various factors and trends from both the demand and supply sides in the AI in life science market.
AI in life sciences refers to the application of artificial intelligence technologies to analyze complex biological, clinical, and healthcare data to improve research, development, and patient outcomes. It uses advanced algorithms and computational models to support areas such as drug discovery, disease diagnosis, clinical trials, and precision medicine, enabling faster insights and more efficient decision-making across the life sciences ecosystem.
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