US AI in Precision Therapies Market - Strategic Insights and Forecasts (2025-2030)

Description

US AI in Precision Therapies Market is anticipated to expand at a high CAGR over the forecast period.

The integration of Artificial Intelligence into precision therapies represents a paradigm shift within the U.S. healthcare ecosystem, moving from a reactive, one-size-fits-all treatment model to one that is proactive, predictive, and highly personalized. This transformation is underpinned by the convergence of massive, multimodal healthcare data spanning genomic sequencing, electronic health records (EHRs), and advanced medical imaging with sophisticated machine learning and deep learning algorithms. The core value proposition of AI in this context is its ability to extract clinically actionable insights from this high-dimensional data at scale, a task beyond human cognitive capability. This capability is rapidly translating into new diagnostic tools, accelerated drug target identification, and optimized treatment regimens, positioning the United States as the global epicenter for the commercialization and clinical deployment of these advanced technologies. The market’s current trajectory is a direct consequence of decades of investment in foundational biomedical research and a highly digitized, although fragmented, healthcare infrastructure.

United States AI in Precision Therapies Market Analysis

Growth Drivers

The surge in chronic disease prevalence, particularly complex cancers and neurological disorders, creates a compelling clinical imperative for better-targeted treatments, directly increasing the demand for AI-driven precision tools. Advancements in next-generation sequencing (NGS) and multi-omics technologies generate vast quantities of patient data, making manual analysis infeasible; this computational bottleneck directly creates demand for AI platforms capable of processing, integrating, and interpreting genomic, proteomic, and clinical data to identify novel biomarkers. Furthermore, the rising adoption of electronic health records (EHRs) and other digital health platforms in U.S. hospitals has led to the accumulation of real-world clinical data, establishing the necessary data infrastructure for training and deploying commercial AI models in a clinical setting. This data accessibility is a core catalyst for market expansion, accelerating the commercial development cycle for AI-powered diagnostics and treatment optimization software.

Challenges and Opportunities

A critical challenge constraining market demand is the enduring concern over data privacy, security, and governance, which complicates the sharing and aggregation of the necessary large, diverse datasets required for robust AI model training. This constraint restricts model generalizability across diverse U.S. patient populations and therapeutic areas. Conversely, a significant opportunity lies in the burgeoning field of generative AI for de novo drug design and small-molecule optimization. AI models can simulate chemical reactions and predict compound behavior in the body, dramatically reducing the time and cost of the preclinical drug discovery phase. The capability to design novel therapeutic candidates computationally is a strong demand driver, particularly among pharmaceutical and biotechnology companies seeking to mitigate the high failure rates and protracted timelines of traditional drug development.

Raw Material and Pricing Analysis

The AI in Precision Therapies market is fundamentally a software and services market, not a physical product market. The core assets are algorithms, licensed data libraries, cloud computing resources, and the intellectual property encapsulated in the trained models. Therefore, a traditional raw material or supply chain analysis is not applicable. The primary cost components are human capital (data scientists, bioinformaticians, and software engineers) and computational infrastructure. Pricing for AI solutions is typically structured through subscription licensing, pay-per-use models (e.g., per genomic analysis or per clinical trial match), or large-scale data licensing agreements with pharmaceutical partners. Pricing elasticity is high, driven by demonstrable return on investment (ROI) in clinical outcomes, operational efficiency, and speed-to-market in drug development.

Supply Chain Analysis

The AI in Precision Therapies supply chain is fundamentally digital, centered on data origination, processing, and delivery of insights. The chain begins with data generation (U.S. hospitals, academic centers, and diagnostic labs) feeding multimodal patient data into AI development hubs (technology companies, AI startups). Logistical complexity centers not on physical transport, but on securing compliant, high-bandwidth data transfer and maintaining data interoperability across disparate EHR systems and sequencing platforms. Key dependencies include high-performance computing (HPC) hardware providers, specialized cloud service providers (like Amazon Web Services or Microsoft Azure), and a highly skilled workforce for model training and clinical validation. This chain is vertically integrated by key players who control both the data ingestion pipeline and the end-user clinical decision support platform.

In-Depth Segment Analysis

By Application: Oncology

Oncology is the dominant segment, driven by the inherent heterogeneity of cancer and the high clinical and economic costs of ineffective treatments, creating an urgent demand for predictive precision tools. AI addresses the core oncology challenge: predicting a patient's response to specific therapies, such as immunotherapies or targeted small-molecule drugs. The integration of genomic, transcriptomic, and proteomic data with complex imaging (Radiomics) requires machine learning to identify unique mutational signatures and micro-environmental features that indicate a therapeutic response. This integration powers tools for molecular tumor boards, clinical trial matching, and monitoring minimal residual disease (MRD). For example, AI platforms that can rapidly match a patient's specific tumor profile to eligible, open clinical trials directly increase demand for the AI service by reducing patient time-to-treatment and improving trial enrollment efficiency for pharmaceutical companies. The high volume of structured and unstructured data generated in U.S. cancer centers further consolidates this segment's lead.

By End-User: Hospitals and Clinics

Hospitals and Clinics represent a major end-user segment due to their role as the primary points of care delivery and data origination. The core demand driver for these institutions is the imperative to improve clinical efficiency, reduce diagnostic errors, and demonstrate superior patient outcomes in a value-based care model. AI solutions offer a direct path to this goal through automating tasks like genomic variant prioritization, diagnostic image analysis (reducing radiologist workload), and real-time risk stratification of patients in the Intensive Care Unit (ICU). This adoption is often facilitated by deep, multi-year strategic partnerships between healthcare systems (e.g., Mayo Clinic, Cleveland Clinic) and technology providers (e.g., IBM, NVIDIA). The integration of AI tools directly into existing EHR and clinical workflow systems, via APIs or proprietary platforms, is critical, as ease of use and reduced time-to-insight directly increase the pull-through demand from front-line clinicians. Furthermore, the adoption of AI-enabled remote patient monitoring (RPM) is driven by the need to manage chronic conditions more effectively and reduce costly readmissions.

Competitive Environment and Analysis

The U.S. AI in Precision Therapies market is characterized by a high-stakes competitive environment, featuring a mix of large, diversified technology firms and specialized, data-centric startups. Competition centers on access to proprietary, high-quality, multimodal clinical data sets, the defensibility of the core AI models, and deep clinical integration into existing healthcare workflows.

Company Profile: IBM Watson Health (IBM Corporation)

IBM's strategic positioning leverages its legacy in enterprise-level information technology and its deep-seated presence within the healthcare and life sciences sectors. Following the divestiture of certain assets, the company's current focus is on delivering AI-powered hybrid cloud and data platforms that underpin precision medicine workflows for major institutions and pharmaceutical partners. Its key product, the IBM watsonx platform, provides life sciences organizations with a suite of AI-enabled tools for generative AI in drug discovery, data governance, and secure data fabric. IBM’s value proposition is its ability to handle massive, complex, and highly regulated data sets, allowing researchers to use AI models to accelerate product development and optimize supply chains. This enterprise-grade focus positions IBM as a key infrastructure provider rather than a direct clinical-software vendor.

Company Profile: Tempus Labs, Inc.

Tempus Labs is strategically positioned as a data-centric AI company, aiming to structure and analyze the world’s largest library of clinical and molecular data. The company's core strategy is a vertical integration model: offering genomic sequencing services (like the Tempus xT solid tumor assay and Tempus xF liquid biopsy) to collect multimodal patient data, which is then used to train proprietary AI algorithms. Its products include AI-powered platforms for oncology treatment selection, clinical trial matching, and genomic analysis. A core service is the licensing of its data and analytical tools (Insights business) to large pharmaceutical companies for use in drug discovery and development. The acquisition of Ambry Genetics in early 2025 significantly augmented its inherited risk and germline testing capabilities, further consolidating its data moat and expanding its platform into new disease areas such as cardiology and rare diseases.

Company Profile: Nvidia Corporation

Nvidia Corporation acts as an essential enabling technology provider, supplying the fundamental hardware and software tools necessary for training and deploying AI models at scale. Its strategic positioning is rooted in its dominance of the high-performance computing (HPC) and Graphical Processing Unit (GPU) market. Key products, such as the NVIDIA Clara platform and the NVIDIA DGX systems, provide the necessary computational backbone for genomic sequencing analysis, medical imaging AI, and large-scale drug discovery. Through collaborations with companies like Illumina and major academic centers such as the Mayo Clinic, Nvidia ensures that its hardware and AI frameworks (e.g., BioNeMo for generative AI in biology) are integrated directly into the research and clinical data pipelines. This infrastructural dominance makes Nvidia an indispensable partner, fueling demand for AI solutions by continuously reducing the computational time and cost required for developing new precision models.

Recent Market Developments

The following verified developments highlight the market’s continued acceleration through strategic consolidation and technology expansion in the U.S. precision therapies sector:

• February 2025: Tempus AI, Inc. successfully closed the acquisition of Ambry Genetics. This merger was a strategic move to immediately expand Tempus’s inherited cancer risk and germline testing capabilities, integrating Ambry’s clinical testing volume and data into the Tempus AI platform and diversifying its end-user offerings beyond oncology into pediatrics and rare diseases.
• January 2025: NVIDIA announced new partnerships with healthcare leaders, including the Mayo Clinic and Illumina, to advance AI-driven medical innovation. Specifically, the collaboration with Mayo Clinic focused on accelerating the development of AI-powered digital pathology models using NVIDIA DGX systems to improve cancer diagnosis and treatment planning, leveraging Mayo Clinic's extensive dataset of 20 million whole-slide images.
• March 2024: Google Cloud launched two new AI-powered tools aimed at speeding up the drug discovery process. The "Target and Lead Identification Suite" helps researchers identify biological targets, and the "Multiomics Suite" supports genomic data analysis, demonstrating a strategic product launch focused on serving pharmaceutical and biotech companies in their precision medicine R&D workflows.

United States AI in Precision Therapies Market Segmentation

The U.S. market for AI in Precision Therapies is formally segmented as follows:

• By Therapy Type
  • Personalized Medicine
  • Targeted Therapies
  • Gene Therapies
  • Cell Therapies
  • Immunotherapies
  • Others
• By Application
  • Oncology
  • Neurology
  • Cardiology
  • Infectious Diseases
  • Rare Diseases
  • Autoimmune Diseases
  • Others
• By End-Users
  • Hospitals and Clinics
  • Pharmaceutical and Biotechnology Companies
  • Research Institutes and Academic Centers
  • Others

Table Of Contents

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter's Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. UNITED STATES AI IN PRECISION THERAPIES MARKET BY THERAPY TYPE

5.1. Introduction

5.2. Personalized Medicine

5.3. Targeted Therapies

5.4. Gene Therapies

5.5. Cell Therapies

5.6. Immunotherapies

5.7. Others

6. UNITED STATES AI IN PRECISION THERAPIES MARKET BY APPLICATION

6.1. Introduction

6.2. Oncology

6.3. Neurology

6.4. Cardiology

6.5. Infectious Diseases

6.6. Rare Diseases

6.7. Autoimmune Diseases

6.8. Others

7. UNITED STATES AI IN PRECISION THERAPIES MARKET BY END-USERS

7.1. Introduction

7.2. Hospitals And Clinics

7.3. Pharmaceutical And Biotechnology Companies

7.4. Research Institutes And Academic Centers

7.5. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

8.1. Major Players and Strategy Analysis

8.2. Market Share Analysis

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Competitive Dashboard

9. COMPANY PROFILES

9.1. IBM Watson Health (IBM Corporation)

9.2. Google Health (Alphabet Inc.)

9.3. Tempus Labs, Inc.

9.4. Nvidia Corporation

9.5. Deep Genomics, Inc.

9.6. Microsoft Corporation

9.7. Owkin, Inc.

9.8. Benevolentai Ltd.

9.9. Recursion Pharmaceuticals, Inc.

9.10. Cloud Pharmaceuticals, Inc.

10. APPENDIX

10.1. Currency

10.2. Assumptions

10.3. Base and Forecast Years Timeline

10.4. Key benefits for the stakeholders

10.5. Research Methodology

10.6. Abbreviations

LIST OF FIGURES

LIST OF TABLES

Companies Profiled

IBM Watson Health (IBM Corporation)

Google Health (Alphabet Inc.)

Tempus Labs, Inc.

Nvidia Corporation

Deep Genomics, Inc.

Microsoft Corporation

Owkin, Inc.

Benevolentai Ltd.

Recursion Pharmaceuticals, Inc.

Cloud Pharmaceuticals, Inc.  

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