Introduction:

The AI in precision medicine market size commands a value of USD 6.49 billion in 2025 and is forecasted to reach USD 16.21 billion by 2030, advancing at a 20.01% CAGR, reflecting strong confidence in machine learning and data-driven care models. The forecast period sees a compound annual growth rate that validates optimism around AI’s role in patient stratification, targeted therapy selection, and clinical decision support.

Precision medicine, which centers on customizing care based on individual genetic, environmental, and lifestyle factors, is increasingly enabled by AI algorithms that sift through large, complex biomedical datasets. As more biobanks, electronic health record networks, and genomic sequencing projects scale, demand is rising for AI platforms that can translate raw data into actionable insights.

Key Trends in the AI in Precision Medicine Market

Deep Learning Holds Lead, NLP Gaining Traction

Within the ai in precision medicine market, deep learning currently captures the largest share among AI technologies, owing to its strength in pattern recognition across genomic, imaging, and multi-omic data. Meanwhile, natural language processing (NLP) is forecast to register one of the fastest growth rates as clinical narratives, pathology reports, and EHR text are integrated into decision support models. As developers enrich multimodal pipelines, the balance between structured omics data and unstructured text will become a sweet spot.

Software Dominance, Services Support Growth

The software component drives a major share of the ai in precision medicine market share, reflecting demand for platforms, analytics engines, and subscription models. Concurrently, services (integration, validation, consulting) are growing at a higher relative rate, as health systems and research groups require help deploying AI, meeting regulatory demands, and integrating with existing infrastructure.

Oncology Leads Therapeutic Use Cases

Among therapeutic applications, oncology remains the dominant segment, accounting for a significant fraction of the ai in precision medicine market size today. AI models assist in biomarker discovery, therapy matching, tumor evolution forecasting, and resistance prediction. Emerging segments such as respiratory diseases are expected to see high growth, combining imaging, spirometry, genomic, and wearable inputs to offer more precise management of COPD, asthma, and interstitial lung disease.

Check out more details and stay updated with the latest industry trends, including the Japanese version for localized insights: https://www.mordorintelligence.com/ja/industry-reports/ai-in-precision-medicine-market?utm_source=abnewswire

Market Segmentation in the AI in Precision Medicine Market

To understand value flows and investment priorities, the ai in precision medicine market can be segmented by several key dimensions:

By Component

• Software

• Services

• Hardware

By Technology

• Deep Learning

• Querying Method

• Natural Language Processing

• Context-Aware Processing

By Therapeutic Application

• Oncology

• Cardiology

• Neurology

• Respiratory

• Other Therapeutic Applications

By Geography / Region

• North America

• Asia-Pacific

• Europe

• South America

• Middle east&Africa

Key Players in the AI in Precision Medicine Market

• IBM Corp.

• Google (Alphabet incl. DeepMind)

• NVIDIA Corporation

• Tempus AI

• Illumina Inc.

Explore more insights on ai in precision medicine market competitive landscape: https://www.mordorintelligence.com/industry-reports/ai-in-precision-medicine-market/companies?utm_source=abnewswire

Conclusion

As the ai in precision medicine market gains momentum, stakeholders across healthcare, biotech, diagnostics, and health IT must position strategically. Growth is underpinned by increasing availability of genomic and clinical data, demand for more tailored interventions, and the maturation of AI technologies such as deep learning and NLP. The ai in precision medicine market growth hinges on translating algorithmic outputs into real-world care decisions, while preserving patient privacy and regulatory trust.

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