U.S. AI in Precision Medicine Market Size, Share, Opportunities, And Trends By Technology (Machine Learning, Natural Language Processing (NLP), Deep Learning, Big Data Analytics, Others), By Application (Drug Discovery and Development, Oncology, Genetic Testing, Rare Diseases, Infectious Diseases, Others), And By End-User (Pharmaceutical and Biotechnology Companies, Research Institutes and Academic Centers, Healthcare Providers, Others) – Forecasts From 2025 To 2030

U.S. AI in Precision Medicine Market Size:

The US AI in the Precision Medicine market is anticipated to rise notably during the forecast period.

U.S. AI in Precision Medicine Market Highlights:

• AI enhances precision medicine by analyzing complex genomic and clinical data for personalized treatments.
• Recent partnerships advance AI-driven diagnostics and multi-omic data integration for better outcomes.
• FDA approvals of AI tools signal a supportive regulatory environment for precision medicine.
• Challenges like data privacy and algorithmic bias require ongoing attention to ensure equity.

U.S. AI in the Precision Medicine Market Introduction:

The integration of AI in precision medicine is transforming the U.S. healthcare landscape, enabling highly personalized treatment strategies that leverage vast datasets to optimize patient outcomes. Precision medicine, which customizes medical interventions to individual characteristics such as genetics, lifestyle, and environmental factors, relies on AI’s ability to analyze complex biological and clinical data with unprecedented speed and accuracy. In the U.S., this convergence is driving advancements in diagnostics, drug development, and clinical decision-making, positioning the nation as a global leader in innovative healthcare solutions.

Precision medicine represents a paradigm shift from one-size-fits-all healthcare to individualized care based on a patient’s unique profile. AI enhances this approach by processing multimodal data, such as genomic, proteomic, clinical, and imaging datasets, to identify patterns and correlations that inform diagnosis, prognosis, and treatment. For example, AI algorithms are used in genome-informed prescribing, where machine learning predicts which patients may benefit from specific medications based on genetic markers, enabling proactive genotyping and personalized dosing. This was demonstrated in recent advancements where AI-driven models have improved the interpretation of genomic data for targeted therapies, reducing trial-and-error in treatment plans.

AI’s impact extends to radiogenomics, a field that correlates cancer imaging features with gene expression to predict treatment outcomes, such as toxicity risks following radiotherapy. For instance, AI models have been developed to predict isocitrate dehydrogenase genotypes in gliomas using MRI datasets, enhancing non-invasive diagnostics. Similarly, AI-powered tools like those developed by NVIDIA, such as Clara Discovery, are accelerating drug discovery by modeling molecular interactions and predicting protein structures, streamlining the identification of novel therapeutic targets.

In clinical settings, AI integrates with electronic health records (EHRs) and real-world evidence (RWE) platforms to support real-time decision-making. Tools like Prenosis’s Sepsis ImmunoScore™, which received FDA De Novo authorization in April 2024, use AI to assess sepsis risk, demonstrating how predictive analytics can improve patient outcomes in critical care. Additionally, wearables and digital biomarkers, enhanced by AI, enable continuous monitoring and early detection of physiological changes, supporting proactive interventions and chronic disease management.

Recent collaborations and technological advancements underscore the momentum of AI in precision medicine. In April 2025, Illumina Inc. partnered with Tempus AI to advance next-generation sequencing (NGS) by integrating AI-driven genomic algorithms with multimodal data platforms, aiming to enhance clinical adoption of precision diagnostics. Similarly, in January 2025, Guardant Health and ConcertAI launched a multi-modal real-world data solution combining genomic and epigenomic tumor profiling with EMR data, targeting improved cancer care across all disease stages. These initiatives reflect a broader trend of stakeholder partnerships among technology firms, healthcare providers, and research institutions to drive innovation.

In October 2024, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) hosted a workshop titled “AI in Precision Medicine for Diabetes and Other Chronic Diseases,” bringing together biomedical researchers and AI experts to address challenges and opportunities in developing AI-driven solutions for chronic disease management. This event highlighted the growing emphasis on cross-disciplinary collaboration to tackle complex healthcare challenges. Additionally, advancements in AI-powered imaging, such as Quibim’s QP-Brain tool for brain MRI analysis, are improving the detection of neurological conditions like brain atrophy and lesions, offering quantifiable insights for personalized treatment plans.

U.S. AI in the Precision Medicine Market Drivers:

• Rising Demand for Personalized Therapies

The increasing prevalence of chronic and genetic diseases, such as cancer and diabetes, has heightened the need for tailored medical interventions. Precision medicine, which personalizes treatments based on individual patient characteristics like genetics and lifestyle, relies heavily on AI to process vast datasets and identify optimal therapeutic strategies. AI-driven tools, such as those used in genome-informed prescribing, analyze genetic markers to predict treatment efficacy, reducing trial-and-error in clinical practice. For instance, AI models are being employed to match patients with targeted cancer therapies based on tumor genomic profiles, improving response rates and minimizing adverse effects. This demand is further fueled by patient and provider expectations for treatments that address individual variability, driving investment in AI technologies that enable precision diagnostics and therapeutics.

• Advancements in Genomics and Multi-Omics

Rapid progress in genomics, coupled with AI’s computational capabilities, is accelerating the integration of multi-omic data (genomic, proteomic, metabolomic) to uncover disease mechanisms and identify novel biomarkers. AI algorithms enhance the analysis of next-generation sequencing (NGS) data, enabling researchers to pinpoint genetic mutations and predict disease progression with greater accuracy. For example, recent advancements in AI-driven radiogenomics correlate imaging features with gene expression, facilitating non-invasive diagnostics for conditions like gliomas. Collaborations, such as the April 2025 partnership between Illumina Inc. and Tempus AI, are leveraging AI to integrate genomic and clinical data, streamlining the development of targeted therapies and expanding the application of multi-omics in clinical settings.

• Supportive Regulatory Environment

The U.S. FDA has established a proactive framework for regulating AI and machine learning (ML) applications in healthcare, fostering market confidence and adoption. Recent FDA approvals, such as Prenosis’s Sepsis ImmunoScore™ in April 2024 and Ibex Medical Analytics’ Prostate Detect in February 2025, demonstrate a clear pathway for AI-driven diagnostics and decision-support tools. The FDA’s guidance on AI/ML-based software as a medical device (SaMD) provides clarity on validation and deployment requirements, encouraging innovation while ensuring patient safety. This regulatory support is critical for scaling AI solutions in precision medicine, as it enables developers to navigate compliance challenges and bring products to market efficiently.

U.S. AI in the Precision Medicine Market Restraints:

• Data Privacy and Security Concerns

AI in precision medicine relies on vast amounts of sensitive patient data, including genomic profiles, clinical records, and real-world evidence. This raises significant concerns about data privacy, security breaches, and the potential re-identification of anonymized data. The absence of harmonized federal regulations for health data privacy, coupled with varying state-level laws, complicates compliance for organizations deploying AI solutions. High-profile data breaches in healthcare, such as those reported in recent years, have eroded patient trust and heightened scrutiny on data handling practices. Addressing these concerns requires robust encryption, secure data-sharing protocols, and transparent governance frameworks, which can increase development costs and delay market entry.

• Fairness and Bias in AI Models

AI models in precision medicine are only as effective as the data they are trained on, and biases in training datasets can lead to inequitable healthcare outcomes, particularly for underrepresented populations. For example, if datasets lack diversity in ethnicity, socioeconomic status, or geographic representation, AI algorithms may produce skewed predictions, such as inaccurate risk assessments for certain groups. This issue is compounded by the complexity of validating AI models across diverse populations, which requires significant resources and expertise. Ethical concerns about fairness, coupled with the need for rigorous bias mitigation strategies, pose a barrier to widespread adoption. Ongoing efforts, such as those discussed at the NIDDK workshop in October 2024, emphasize the importance of inclusive data practices to address these challenges.

U.S. AI in the Precision Medicine Market Segmentation Analysis:

• The application of Machine Learning is rising rapidly

Machine learning (ML) is the cornerstone of AI applications in precision medicine, enabling the analysis of vast and complex datasets to uncover patterns that inform diagnosis, prognosis, and treatment. ML algorithms excel at processing structured data, such as genomic sequences, electronic health records (EHRs), and clinical trial outcomes, to predict patient-specific responses to therapies. For example, ML models are used in genome-informed prescribing to identify genetic markers that are influencing drug efficacy, reducing adverse effects, and optimizing dosing. A notable application is in cancer care, where ML algorithms analyze tumor genomic profiles to recommend targeted therapies, as seen in Tempus’s xT platform, which integrates clinical and molecular data to enhance therapeutic matching. ML also supports predictive analytics in critical care, such as Prenosis’s Sepsis ImmunoScore™ for assessing sepsis risk using clinical biomarkers. Additionally, ML facilitates multi-omics integration, combining genomic, proteomic, and metabolomic data to identify disease mechanisms and biomarkers, as highlighted in recent research from the National Institutes of Health. The scalability and adaptability of ML make it the dominant technology in precision medicine, driving innovation in personalized diagnostics and treatment planning.

• The Oncology department is anticipated to increase its use of AI

Oncology is the leading application of AI in precision medicine, driven by the complexity of cancer and the need for individualized treatment strategies. AI enhances oncology through molecular diagnostics, tumor profiling, and treatment optimization. For instance, AI-driven platforms like Guardant Health’s liquid biopsy assays, combined with ConcertAI’s real-world data solutions, integrate genomic and epigenomic data to guide targeted therapies across various cancer stages. Deep learning models, a subset of AI, analyze radiogenomic data to correlate imaging features with gene expression, enabling non-invasive prediction of tumor characteristics, such as isocitrate dehydrogenase genotypes in gliomas. In drug discovery, AI accelerates the identification of novel therapeutic targets by modeling protein-ligand interactions, as demonstrated by NVIDIA’s Clara Discovery platform. Additionally, AI supports clinical trial matching by identifying eligible patients based on genetic and clinical profiles, improving trial efficiency and patient outcomes. The high prevalence of cancer and the demand for personalized treatments make oncology a critical focus area for AI-driven precision medicine in the U.S.

• Pharmaceutical and Biotechnology companies are growing notably

Pharmaceutical and biotechnology companies are the primary end-users of AI in precision medicine, leveraging these technologies to streamline drug discovery, development, and commercialization. These companies utilize AI to analyze large-scale genomic and clinical datasets, identifying novel drug targets and optimizing clinical trial designs. For example, Tempus’s collaboration with BioNTech uses AI to analyze multimodal datasets, enhancing oncology drug development by identifying actionable biomarkers. AI also supports virtual screening and de novo molecule design, as seen in Atomwise’s AtomNet platform, which uses deep learning to predict effective molecules for diseases like cancer and infectious disorders. Furthermore, these companies employ AI to improve patient stratification in clinical trials, ensuring that therapies are tested on populations most likely to benefit, thus reducing costs and time-to-market. The significant R&D investments by U.S.-based pharmaceutical giants, coupled with partnerships with AI-driven firms like Tempus and NVIDIA, underscore their leading role in adopting AI for precision medicine.

U.S. AI in the Precision Medicine Market Key Developments:

• Illumina and Tempus AI Strategic Collaboration (2025): Illumina Inc., a leader in genomic sequencing, partnered with Tempus AI to accelerate the clinical adoption of next-generation sequencing (NGS) tests. This collaboration combines Illumina’s advanced AI technologies with Tempus’s multimodal data platform to train genomic algorithms, enhancing precision diagnostics and therapeutic decision-making.
• Guardant Health and ConcertAI Multi-Modal Data Solution (2025): Guardant Health and ConcertAI launched a multi-modal real-world data solution that integrates genomic and epigenomic tumor profiling with electronic medical record (EMR) data. This AI-driven platform supports precision oncology by providing actionable insights across all cancer stages, from early detection to treatment selection. The solution leverages AI to analyze complex datasets, enabling healthcare providers to tailor therapies based on individual patient profiles, marking a significant advancement in oncology applications.
• Ibex Medical Analytics’ Prostate Detect FDA Clearance (2025): Ibex Medical Analytics received FDA 510(k) clearance for its Prostate Detect, an AI-powered in vitro diagnostic tool that generates heatmaps to identify small or rare prostatic cancers in pathology slides. This tool enhances diagnostic accuracy by assisting pathologists in detecting challenging cases, supporting precision medicine in oncology.
• FDA Authorization of Prenosis’s Sepsis ImmunoScore™ (2024): Prenosis received FDA De Novo marketing authorization for its Sepsis ImmunoScore™, an AI-powered diagnostic tool designed to assess the risk of sepsis in critical care settings. This tool integrates 22 clinical and biomarker parameters from EHRs to provide real-time risk stratification, enabling clinicians to make informed decisions for personalized patient management.

Segmentation:

• By Technology
  • Machine Learning
  • Natural Language Processing (NLP)
  • Deep Learning
  • Big Data Analytics
  • Others                 
• By Application
  • Drug Discovery and Development
  • Oncology
  • Genetic Testing
  • Rare Diseases
  • Infectious Diseases
  • Others     
• By End-user
  • Pharmaceutical and Biotechnology Companies
  • Research Institutes and Academic Centers
  • Healthcare Providers
  • Others     

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. US AI IN PRECISION MEDICINE BY TECHNOLOGY

4.1. Machine Learning

4.2. Natural Language Processing (NLP)

4.3. Deep Learning

4.4. Big Data Analytics

4.5. Others                 

5. US AI IN PRECISION MEDICINE BY APPLICATION

5.1. Drug Discovery and Development

5.2. Oncology

5.3. Genetic Testing

5.4. Rare Diseases

5.5. Infectious Diseases

5.6. Others     

6. US AI IN PRECISION MEDICINE BY END-USER

6.1. Pharmaceutical and Biotechnology Companies

6.2. Research Institutes and Academic Centers

6.3. Healthcare Providers

6.4. Others     

7. COMPETITIVE ENVIRONMENT AND ANALYSIS

7.1. Major Players and Strategy Analysis

7.2. Market Share Analysis

7.3. Mergers, Acquisitions, Agreements, and Collaborations

7.4. Competitive Dashboard

8. COMPANY PROFILES

8.1. NVIDIA Corporation 

8.2. Illumina, Inc. 

8.3. Tempus AI, Inc. 

8.4. Guardant Health, Inc. 

8.5. Ibex Medical Analytics Ltd. 

8.6. Prenosis, Inc. 

8.7. Alphabet Inc. 

8.8. GE HealthCare Technologies Inc. 

8.9. ConcertAI, LLC 

8.10. BioXcel Therapeutics, Inc.

NVIDIA Corporation 

Illumina, Inc. 

Tempus AI, Inc. 

Guardant Health, Inc. 

Ibex Medical Analytics Ltd. 

Prenosis, Inc. 

Alphabet Inc. 

GE HealthCare Technologies Inc. 

ConcertAI, LLC 

BioXcel Therapeutics, Inc.