Artificial Intelligence In Precision Medicine Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Technology (Deep Learning, Querying Method, Natural Language Processing, Context-Aware Processing), By Component (Hardware, Software, Service), By Therapeutic Application (Oncology, Cardiology, Neurology, Respiratory, Other), By Region & Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 1.78 Billion
CAGR (2026-2031)	9.81%
Fastest Growing Segment	Hardware
Largest Market	North America
Market Size (2031)	USD 3.12 Billion

Market Overview

The Global Artificial Intelligence In Precision Medicine Market is projected to grow from USD 1.78 Billion in 2025 to USD 3.12 Billion by 2031 at a 9.81% CAGR. Artificial intelligence in precision medicine is defined as the application of computational algorithms, specifically machine learning and deep learning, to analyze complex biological and clinical datasets for tailoring healthcare interventions to individual patient characteristics. This technology allows for the precise customization of medical decisions and treatments based on a patient's unique genetic makeup, lifestyle factors, and environmental influences.

The primary drivers supporting this market include the exponential growth of large-scale genomic data, the economic imperative to reduce high costs associated with drug discovery, and the increasing prevalence of chronic diseases requiring targeted therapies. According to the 'American Medical Association', in '2025', '66% of physicians reported using healthcare artificial intelligence in 2024, representing a significant increase in adoption rates from the previous year'. Despite this growth, the market faces a significant challenge regarding data privacy and security, as the utilization of sensitive patient information raises regulatory hurdles that could impede widespread expansion.

Key Market Drivers

The need for cost containment and efficiency in healthcare delivery is a primary force propelling the adoption of artificial intelligence. Healthcare systems face mounting financial pressures, driving organizations to implement AI solutions that automate administrative workflows and optimize resource allocation. These technologies streamline operations, allowing providers to focus on patient care while significantly lowering expenditures. According to NVIDIA, March 2025, in the 'State of AI in Healthcare and Life Sciences' report, 73% of organizations cited reductions in operating costs as a key benefit of AI adoption. This efficiency extends beyond administration into clinical settings, where AI tools are increasingly used to validate diagnostic results and stratify patient risks more effectively.

The acceleration of drug discovery and development timelines fundamentally reshapes the precision medicine landscape by addressing the inefficiencies of traditional pharmaceutical R&D. Conventional methods often require over a decade to bring a therapy to market, but AI-driven platforms can rapidly identify viable targets and design novel molecules with higher success probabilities. According to Insilico Medicine, February 2025, in the 'Preclinical Drug Discovery Benchmarks' announcement, the company averaged a 13-month timeline to nominate preclinical candidates, a significant reduction from the traditional 2.5 to 4-year process. Furthermore, real-world applications validate these technological gains; according to AstraZeneca, March 2025, in findings presented at the 'European Lung Cancer Congress', its AI-powered chest X-ray tool demonstrated a 54.1% positive predictive value, illustrating the technology's capacity to enhance diagnostic precision and clinical trial screening efficiency.

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Key Market Challenges

The challenge regarding data privacy and security presents a substantial barrier to the expansion of the Global Artificial Intelligence in Precision Medicine Market. Precision medicine relies heavily on the aggregation of massive, heterogeneous datasets—including genomic sequences, electronic health records, and real-time patient monitoring data—to train complex algorithms. However, the highly sensitive nature of this information necessitates strict adherence to rigorous regulatory frameworks. These compliance requirements often create data silos, making it difficult for organizations to legally share the diverse datasets required to validate AI models across different demographics. Consequently, the development of universally applicable algorithms is stifled, as developers struggle to access the breadth of data needed to minimize bias and ensure clinical accuracy.

The escalating threat of cyberattacks further exacerbates these hurdles, causing healthcare providers to hesitate in adopting cloud-based AI solutions. According to the 'Healthcare Information and Management Systems Society', in '2025', '75% of healthcare cybersecurity professionals identified data privacy as a top concern regarding the future implementation of artificial intelligence in healthcare settings'. This widespread apprehension forces institutions to prioritize defensive security measures over innovation, diverting critical investment away from AI integration. As a result, the fear of data breaches and the associated legal repercussions directly constrains the market's trajectory by increasing operational costs and extending the timeline for the commercial deployment of new technologies.

Key Market Trends

The expansion of AI-driven radiomics and computational pathology is fundamentally altering diagnostic workflows by enabling the quantitative assessment of complex tissue characteristics that subjective human review often misses. This trend involves deploying deep learning algorithms to analyze digitized histological slides and medical images, thereby standardizing biomarker quantification and enhancing the precision of patient stratification for targeted therapies. By uncovering sub-visual patterns in the tumor microenvironment, these tools reduce inter-observer variability and support more accurate treatment decisions. According to Proscia, June 2025, in the 'Digital Pathology and AI Highlights from ASCO 2025' report, a multi-center study demonstrated that AI-assisted analysis boosted diagnostic agreement among pathologists to 86.4% for HER2-low breast cancer scoring, significantly improving upon traditional manual assessment methods.

A surge in strategic biopharma-tech partnerships and ecosystems is concurrently accelerating the market, as pharmaceutical companies increasingly collaborate with AI-specialized firms to access large-scale real-world data and proprietary computational platforms. These alliances facilitate the integration of longitudinal clinical datasets with genomic information, creating robust ecosystems that expedite drug discovery and validate precision medicine hypotheses. Rather than building internal capabilities from scratch, biopharma leaders are leveraging these external technology stacks to navigate the complexities of multi-omics data and regulatory compliance. According to Tempus AI, January 2026, in the 'Preliminary 2025 Financial Results' announcement, the company disclosed a total contract value exceeding $1.1 billion and the solidification of over 70 data agreements, underscoring the rapid commercial validation and operational reliance on these collaborative data ecosystems.

Segmental Insights

The Hardware segment is currently recognized as the fastest-growing category within the Global Artificial Intelligence in Precision Medicine Market. This rapid expansion is primarily driven by the escalating demand for high-performance computing infrastructure capable of processing massive datasets, such as genomic sequences and high-resolution medical imaging. As healthcare institutions and pharmaceutical companies increasingly adopt complex deep learning models, there is a critical need for specialized processors, including Graphics Processing Units and Tensor Processing Units, to execute these algorithms efficiently. Consequently, robust hardware foundations are becoming essential to enable real-time diagnostics and accelerate the scalability of AI solutions in personalized medicine.

Regional Insights

North America leads the global artificial intelligence in precision medicine market due to extensive investments in healthcare technology and a strong research ecosystem. The region benefits from the presence of major technology firms and pharmaceutical companies that collaborate to develop targeted therapeutic solutions. Additionally, the United States Food and Drug Administration actively encourages innovation through clear regulatory frameworks for software as a medical device. This supportive regulatory environment, combined with the widespread implementation of electronic health records, enables the rapid integration and validation of artificial intelligence models within clinical settings.

Recent Developments

• In October 2025, Eli Lilly and Company announced the deployment of a powerful new artificial intelligence supercomputer to enhance its drug discovery and development capabilities. Unveiled at a major technology conference, this infrastructure was designed to harness generative AI for simulating complex molecular interactions and identifying biological targets with unprecedented speed. The system aimed to transform the pharmaceutical research process by enabling continuous, data-driven experimentation and bridging the gap between wet labs and computational models. This strategic investment highlighted the company's commitment to integrating physical and digital technologies to drive breakthroughs in precision medicine and therapeutic manufacturing.
• In October 2024, Google Cloud entered into a strategic partnership with Recursion to advance the development of artificial intelligence-enabled drug discovery models. The collaboration involved integrating Google’s advanced Gemini models into the RecursionOS platform to process and analyze massive biological and chemical datasets. This alliance focused on leveraging generative AI to identify novel therapeutic targets and accelerate the creation of precision medicines. By combining industrial-scale biological data with sophisticated AI architecture, the companies aimed to significantly reduce the time and costs associated with traditional drug discovery, ultimately delivering more targeted and effective treatments to patients.
• In June 2024, GE HealthCare unveiled several new precision medicine solutions at the Society of Nuclear Medicine and Molecular Imaging Annual Meeting to address challenges in the healthcare industry. The company introduced Clarify DL, a deep learning-based technology designed to deliver clearer and more accurate bone images, and highlighted the StarGuide digital SPECT/CT system, which utilizes advanced detector designs for three-dimensional imaging. These innovations were launched to support theranostics and personalized care pathways, aiming to help clinicians make more informed treatment decisions while improving operational efficiency in medical facilities dealing with resource constraints.
• In March 2024, Microsoft Corp. expanded its strategic collaboration with NVIDIA to accelerate innovation across the healthcare and life sciences sectors. The partnership focused on integrating the global scale and security of the Azure cloud platform with specialized computing capabilities to enhance clinical research and drug discovery. This initiative aimed to equip healthcare organizations with advanced tools for generative artificial intelligence, facilitating the rapid development of precision medicine solutions. By combining powerful cloud infrastructure with accelerated computing, the companies sought to streamline complex workflows in medical diagnostics and therapies, ultimately striving to improve patient outcomes through more efficient technology.

Key Market Players

• Glanbia Plc
• BioXcel Therapeutics, Inc.
• Sanofi S.A.
• NVIDIA Corp.
• Alphabet Inc.
• IBM Technology corporation
• Microsoft Corporation
• Intel Corp.
• AstraZeneca plc
• GE HealthCare Technologies Inc.
• Enlitic, Inc.

By Technology	By Component	By Therapeutic Application	By Region
Deep Learning Querying Method Natural Language Processing Context-Aware Processing	Hardware Software Service	Oncology Cardiology Neurology Respiratory Other	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Artificial Intelligence In Precision Medicine Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Artificial Intelligence In Precision Medicine Market, By Technology:

• Deep Learning
• Querying Method
• Natural Language Processing
• Context-Aware Processing

• Artificial Intelligence In Precision Medicine Market, By Component:

• Hardware
• Software
• Service

• Artificial Intelligence In Precision Medicine Market, By Therapeutic Application:

• Oncology
• Cardiology
• Neurology
• Respiratory
• Other

• Artificial Intelligence In Precision Medicine Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE