Mordor Intelligence has published a new report on the AI in life sciences market, offering a comprehensive analysis of trends, growth drivers, and future projections.
AI in Life Sciences Market Outlook
According to Mordor Intelligence, the AI in life sciences market size was valued at USD 3.61 billion in 2025 and is estimated to grow from USD 4.51 billion in 2026 to reach USD 13.64 billion by 2031, registering a CAGR of 24.78% during the forecast period (2026–2031). This strong AI in life sciences market growth reflects increasing use of intelligent software tools across pharmaceutical research, diagnostics, clinical development, and healthcare analytics.
The artificial intelligence in life sciences industry is benefiting from improved regulatory clarity and wider availability of clinical and genomic datasets. Regulators are increasingly accepting AI-based biomarkers and analytics outputs as valid evidence, helping accelerate approval pathways. At the same time, federated learning and secure data sharing are enabling researchers to train models on diverse datasets without compromising privacy
Growth Drivers the AI in Life Sciences Market Growth
Regulatory Support Improving Adoption Across Clinical Applications
One of the major AI in life sciences market trends is growing regulatory acceptance of AI-generated biomarkers and analytics tools. Regulatory programs have reduced approval timelines for AI-supported oncology and rare disease applications, allowing developers to reach patients faster. These regulatory initiatives also enable validated AI biomarkers to be reused across multiple research pipelines, improving efficiency and increasing the AI in life sciences market share of AI-enabled clinical programs.
Federated Data Networks Strengthening AI Training and Collaboration
The expansion of federated data platforms is another key factor influencing the artificial intelligence in life sciences market growth. These systems allow organizations to train AI models across multiple datasets without transferring sensitive patient information. This approach ensures data privacy while enabling broader and more accurate training. Federated learning is particularly useful in genomics, imaging, and clinical trial optimization. By connecting hospitals, research institutions, and pharmaceutical companies, these networks improve data accessibility and increase collaboration.
Hyperscaler Partnerships Improving Computational Efficiency
Partnerships between cloud providers and pharmaceutical companies are reducing computational costs and increasing scalability. These collaborations enable faster molecular simulations and improve the efficiency of drug discovery pipelines. Reduced computational barriers allow smaller research organizations to participate in advanced AI-driven discovery, expanding the reach of the AI in life sciences industry. Cloud infrastructure also supports large-scale analytics, helping researchers analyze genomic data and clinical records more efficiently.
Generative AI Enhancing Drug Discovery and Research Capabilities
Generative AI is becoming a major contributor to AI in life sciences market trends, especially in early-stage drug discovery. These tools can generate new molecular structures and predict biological interactions, reducing the time needed for candidate selection. This improves research productivity and increases the success rate of drug development.Generative models also support personalized medicine and biomarker identification. By analyzing large biological datasets,
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AI in Life Sciences Market Segmentation
By Offering
- Software
- Services
- Hardware
By Deployment Model
- Cloud / On-Demand
- On-Premise
By Analytics Type
- Descriptive Analytics
- Predictive Analytics
- Prescriptive Analytics
- Generative AI
By Application
- Drug Discovery
- Medical Diagnosis and Imaging
- Clinical Trials Optimisation
- Biotechnology and Bioprocessing
- Precision and Personalised Medicine
- Patient Monitoring and Real-World Evidence
By End User
- Pharmaceutical and Biotechnology Companies
- Contract Research Organisations (CROs)
- Medical Device Manufacturers
- Academic and Research Institutes
- Healthcare Providers and Payers
By Technology
- Machine Learning
- Natural Language Processing (NLP)
- Computer Vision
- Deep Learning and Neural Networks
- Generative AI Models
By Geography
North America
- United States
- Canada
Europe
- Germany
- United Kingdom
- France
- Nordics
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- South Korea
- Rest of Asia Pacific
South America
- Brazil
- Rest of South America
Middle East and Africa
- Saudi Arabia
- United Arab Emirates
- South Africa
- Rest of Middle East and Africa
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Key Players in the AI in Life Sciences Industry
- IBM Corporation
- NuMedii Inc.
- Atomwise Inc
- AiCure LLC
- Nuance Communications Inc.
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Conclusion
The AI in life sciences market forecast remains highly positive as organizations continue integrating AI into research, diagnostics, and clinical development. AI is helping improve efficiency, reduce development timelines, and enhance research accuracy. These benefits are encouraging broader adoption across pharmaceutical companies, healthcare providers, and research organizations.
Overall, the AI in life sciences market growth is expected to remain strong, driven by continued investment, collaboration, and adoption across global healthcare and research sectors. The increasing role of AI in improving efficiency and supporting scientific discovery will continue to expand the AI in life sciences market size, creating long-term opportunities for technology providers, healthcare organizations, and research institutions worldwide.
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