AI in Precision Medicine Market: Growth, Size, Share, and Trends

Report Code HIT 9224
Published in Nov, 2024, By MarketsandMarkets™
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AI in Precision Medicine Market by Application (Drug Discovery, Screening, Diagnosis, Stratification, Staging, Prognosis, Therapy Selection, Monitoring, Risk Management), Indication (Cancer, CNS, CVS), Tools (ML, NLP), & End User -Global Forecast to 2030

Overview

The AI in precision medicine market is projected to reach USD 3.92 billion by 2030 from USD 0.78 billion in 2024, at a CAGR of 30.7% from 2024 to 2030. The use of AI in precision medicine is propelled by the advances made in genomics and multi-omics technologies, allowing AI to process intricate information for the purposes of biomarker identification sas well as treatment customization. For example, Google’s DeepVariant improves the outcome of genomic sequencing which provides support for personalized therapies. The increasing push for personalized medicine approaches in the management of patients with oncology and rare diseases also provides new opportunities for the AI, as it is the case with Tempus which uses AI in providing personalized cancer treatment approaches. Moreover, AI's application in drug development, such as Atomwise's drug compound virtual screening, allows for reduction of the expenses and time in such undertakings.

AI in Precision Medicine Market

Attractive Opportunities in the AI in Precision Medicine Market

Asia Pacific

The Asia Pacific AI in precision medicine market is experiencing significant growth driven by rising R&D investments and a growing burden of chronic diseases. Budget constraints push healthcare providers to adopt cost-effective solutions, while the region's expanding biotechnology sector is further enhancing the demand for AI in precision medicine services.

Growth in precision medicine, AI’s efficiency in drug discovery, integration of Real-World Evidence (RWE) in emerging markets to drive the market significantly.

Emerging applications of AI in precision medicine, biopharmaceuticals, and toxicology are expected to provide lucrative opportunities for market players.

The North America market is expected to grow at a substantial growth rate during the forecast period.

Integration complexity with traditional frameworks, lack of standardization across platforms poses a challenge for collaboration and scalability, limiting market growth opportunities.

Global AI in Precision Medicine Market Dynamics

DRIVER: Advancements in Genomic Research and Data Availability

The large accessibility of genomic data coupled with significant genomic research constitutes a fundamental impetus for the application of artificial intelligence in the sphere of precision medicine. Such an advancement furnishes the essential data and information required for AI algorithms to produce valuable predictions and individualized, therapeutic strategies. DNA-sequencing technologies, especially next-generation sequencing, have very significantly reduced the cost and time needed to decipher human DNA, with the possibility of comprehensive genomics study. For instance, large amounts of genomic datasets with associated clinical and demographic data generated by the 100,000 Genomes Project and the All of Us Research Program opened up rich sources for AI to investigate complex patterns.

Datasets are used by AI to discover genetic mutations with diseases, forecast risks for diseases, and make targeted therapies recommendations. For example, artificial intelligence enabled tools analyse the genomics of tumours to precisely find cancer patients the best immunotherapy therapy with checkpoint drugs based on PD-L1 levels. Deep Genomics, for example, applies AI in drug discovery to find new drugs in rare genetic diseases from viewing thousands of genomic differences. AI models combine genomic data together with others like proteomics and metabolomics and patient health records to assist in the discovery of insights that might be too hard to obtain using manual methods. This integration helps make big progress in finding diseases early, assessing risk levels, and creating personalized treatment plans. This shows how genomic research supports AI use in precision medicine.

RESTRAINT: High Cost of Implementation of Precision Medicine Solutions

The elevated expense of employing precision medicine techniques is a significant deterrent to the adoption of artificial intelligence in precision medicine. This is because it limits the use of such technology to only a small portion of the population, more so in less developed regions. Enhancement or advancements in the field of precision medicine involves various technologies like genomic sequencing, storage and processing of large amounts of data, the use of supercomputers and many other activities that involve dealing with large and complex data sets. These parts are costly to get, keep running, and add into healthcare systems. For instance, the cost of whole-genome sequencing is decreasing, but it is still hundreds to thousands of dollars for each patient, which makes it difficult to apply widely in ordinary healthcare. Furthermore, there is the added expense for large genomic and clinical data needs to be safely stored and managed.

Besides that, AI solutions require money for particular software, highly qualified people like data scientists and bioinformaticians, and strict rules regarding patient data protection and the algorithms, which are transparent. For example, the development of AI-based drug discovery platforms or predictive diagnostic tools takes years with significant financial costs.

In regions with less resources, providers of healthcare services tend to find it difficult to justify these expenses without concrete advantages which in turn inhibits the adoption of AI. Furthermore, the integration of additional AI solutions into the existing healthcare system may also present the issue of hidden costs, such as the reorientation of personnel, the refurbishment of outdated structures, and the coordination of all services. These financial constraints are especially felt by the smaller hospitals and clinics, thus making it difficult for the entire population to inconvenience medicine and use it to the fullest extent possible.

 

OPPORTUNITY: The Role of Predictive Analytics in Advancing AI for Healthcare

The transition towards precision medicine is greatly supported because of the possibility of utilizing patient data in real time to customize interventions even before the disease begins to manifest. Predictive analytics involves the application of advanced computational technology such as artificial intelligence and machine learning to collect, collate and synthesize vast amounts of healthcare data like genomic data, electronic health records, medical images together with their associated environmental and behavioural data among others with the sole purpose of predicting risks of illness, outcomes of treatment and other clinical findings and processes. Such a possibility enables the healthcare system to transform from offering only curative services to providing preventive services which have better patient outcomes and economical utilization of resources.

For instance, machine-learning enabled algorithms to have been created to track the ones likely to be diagnosed with co-morbidities such as diabetes and heart-related diseases in the years coming from their genetic background, lifestyles, and past medical records. This helps to employ the ‘treat early’ theory based on simple lifestyle changes or on mild preventive medications before the illness shows any sign, thereby avoiding further complications, expensive treatment or prolonged management. More so, for instance oncology cancer activity, artificial intelligence techniques have made it easier to predict the likelihood of specific cancer treatments, including immunotherapy, being effective in patients based on their biomarkers and the genetics of their tumours.

Further, predictive analytics supports the drug development process by determining the specific patient subgroups who would respond positively to the investigational therapies, thus maximizing the order and success of the clinical development plan. Tempus and Flatiron Health are two of the appropriate and compelling examples of this where they are using predictive analytics to perform real-world data analysis as a means of bioinformatics to combine genomic and clinical data for better treatment processes. In addition, predictive analytics determines the number of patients expected to be admitted into the hospital and other resources needed for comprehensive operational planning in the hospital systems.

With the bar of predictive analytics rising, AI in precision medicine can aid in providing very efficient, inexpensive and focused care which resolves the pain points in healthcare delivery today.

CHALLENGES: The Impact of Fairness and Bias on AI in Healthcare

Fairness and bias are two issues which significantly hinder the application of AI in precision medicine. This is because, biased algorithms will result in unfair treatment in healthcare especially for underserved groups. Algorithms used in precision medicine are mostly developed with unbalanced data sets which discriminate other population groups. For instance, most genomic data sets are European ancestry dominated leaving people from Africa, Asia, and Latin America with fewer if any data at all. Such disparity creates an enabling environment for the development of AI models for disease risks or treatment interventions of non-European populations which aggravates the inequality in health of people in different geographical regions.

As an example of this, bias is found in genetic risk scores which are used to estimate an individual’s risk for diabetes or heart disease. Such risk scores are known to give a lower estimate in individuals of African ancestry due to the heterogeneity of the database employed to come up with the score which was primarily composed of European ancestry individuals. Likewise, cancer diagnosis systems using artificial intelligence that are trained to analyse images of classifications of demographic groups whose majority Caucasian may not be able to detect the melanoma or breast cancer in Caucasian patients with darker skin tones leading to advanced stages of the illness at diagnosis.

Disparities in certain aspects such as education and ability to access healthcare among those involved in producing the data can also cause bias in AI models. This may be where high-income countries which have greater access to health care services, the health records of people collected may affect the AI models predictions whereas regions with lower sick care provision and limited data access suffer. Solutions to these obstacles include, more heterogeneous data acquisition strategies, development of models that incorporate population pyramids, and validation studies. In the absence of these steps, bias in artificial intelligence may thwart the fundamental objective of precision medicine: delivering fair personalized care to every person.

Global AI in Precision Medicine Market Ecosystem Analysis

AI in precision medicine is changing health care by making use of several large datasets, such as genomic, clinical, and lifestyle data to provide treatment for every single individual. The ecosystem consists of advanced analytics, machine learning algorithms, and collaborations between biotechnological, clinical and improvement technological companies. The major components comprise health care diagnostics dominated by AI tools, drug developing firms incorporating AI in their operations, and healthcare environments that combine EHR with predictive analytics processes. The entire ecosystem depends on interoperability and data privacy, and regulatory restrictions does promote personalized therapies increase effects to the patients while cost incurred is lowered. Stakeholder partnership is vital in upscaling improvement and solving the issues of ethics and technology.

AI in Precision Medicine Market

Source: Secondary Literature, Interviews with Experts, and MarketsandMarkets Analysis

 

The global AI in precision medicine market is segmented on the basis of application, therapeutic area, component, tools, deployment, end user, and region.

By deployment, the cloud-based model accounted for the largest share of the AI in precision medicine market in 2023.

By deployment, the AI in precision medicine market is segmented into cloud-based model, on-premise model and hybrid model. The largest market in the markets for AI in precision medicine is cloud-based deployment since it is economical, scalable, and capable of storing huge and complicated datasets. It allows a wide range of AI tools to be incorporated into health care systems for actionable decisions based on genomic, clinical and patients’ data accessible in real time. Cloud environments, including Microsoft’s Azure or AWS, are prominent and secure virtual tools for collaboration of researchers, providers, and pharmaceutical companies. In addition, the increasing penetration of telehealth and remote monitoring additionally strengthens the market for AI solutions in precision medicine.

Diagnostics & screening segment to register highest growth rate during the forecast period in the AI in precision medicine market, by application

Based on application, the AI in precision medicine market is segmented into drug discovery & development, diagnostics & screening, and therapeutics. Diagnostics & screening segment to register highest growth rate during the forecast period. This is due to the system’s ability such as analyzing complex medial images and genomic cloning more efficiently than ever. AI-Assisted tools support the process of early disease detection for diseases like cancer and genetic disorders where timely intervention is needed. For instance, applications such as Google DeepMind and PathAI enhance the detection of cancer by identifying even the slightest nuances in a medical picture. This increased demand for personalized medicine and its combination with AI on advanced imaging and molecular diagnostics is responsible for the market growth at an accelerated pace for this segment.

North America accounted for the largest share of the AI in precision medicine market in 2023.

The AI in precision medicine market is segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. In 2023, North America accounted for the largest share of the AI in precision medicine market because of its improved healthcare delivery services, strong research and development support, and early adoption of modern technologies. Companies such as Google Health, and Tempus are centered in this region whose focus is to revolutionize the industry by providing AI-enhanced solutions in genomics, diagnostics, and other personalized treatment solutions. Large amounts of government financing and supportive regulations such as the FDA’s guidelines on AI/ML applications in medicine promotes use of AI in medicine. Most of the works affordably focus on Electronic Health Records (EHR) and Real-World Evidence (RWE) which are ideal scaffolds for building AI models. Moreover, the North American approach to oncology, orphan and chronic diseases fits the aims of personalized medicine and the availability of a highly diverse and well-structured population is a plus in building and/or validating the AI models. High expenditures on healthcare as well, as the joint ventures of technology firms, healthcare systems, and educational organizations leave no doubts regarding the leadership of North America in this sphere.

HIGHEST CAGR MARKET IN 2023
US FASTEST GROWING MARKET IN THE REGION
AI in Precision Medicine Market

Recent Developments of AI in Precision Medicine Market

  • In October 2024, Google (US) partnered with Recursion (US) to bring the changes sought in operationalizing precision medicine and extend its boundaries through artificial intelligence and cloud-based deployment of services. In particular, they were focused on improving such processes as drug discovery, data analytics and development of precision medicine by integrating Gemini models into the Recursion system.
  • In August 2024, NVIDIA Corporation (US) launched NVIDIA NIM Agent Blueprints. It is a catalog of pretrained, customizable Al workflows that equip enterprise developers with software for building and deploying generative Al applications for use cases, such as drug discovery and virtual screening, and precision medicine.
  • In August 2024, a partnership with Exscientia (UK) and Recursion (US) was able to enable the use of AI in finding new drug targets, creating new drugs, and performing clinical trials, thereby making breakthroughs in precision medicine. Prior to this merger, the leadership of the combined company announced plans to finish 10 clinical trials in one and a half years.
  • In March 2024, Cognizant (US) collaborated with NVIDIA Corporation (US) towards the end of the same year or earlier to expand the application of generative Al and NVIDIA Boneo platform. The objective of the Tactic Boneo platform is to collaborate in order to address the challenges associated with the use of life science drug discovery processes for faster chemotherapy development and precision medicine by facilitating the development of medicines adapted to specific patient’s characteristics.
  • In July 2023, NVIDIA Corporation (US) invested a total of USD 50 million to Recursion Pharmaceuticals (US) in order to accelerate their plans to develop an aIl systems based on the firms’ BioNeMo platform focused on precision medicine. The primary goal of the joint venture with Recursion was to train Al systems, based on enormous biological and chemical content available in the cloud, owned by Nvidia.
  • In October 2024, Google (US) partnered with Recursion (US) to advance precision medicine through AI and cloud technology. By integrating Gemini models into the RecursionOS platform, they aimed to accelerate drug discovery, improve data analysis, and enable the development of targeted therapies.

Key Market Players

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Scope of the Report

Report Metric Details
Market size available for years 2022-2030
Base Year Considered 2023
Forecast period 2024-2030
Forecast units Million/Billion (USD) include
Segments covered Application, Therapeutic Area, Component, Tools, Deployment, End User
Geographies covered North America, Europe, Asia Pacific, Latin America, and Middle East Africa.

Key Questions Addressed by the Report

What are the major market players covered in the report?
The key players in the AI in precision medicine market include NVIDIA Corporation (US), Google, Inc. (US), Microsoft (US), IBM (US) and Exscientia (UK) among others.
Define AI in precision medicine market.
The AI in precision medicine market utilises AI technologies such as machine learning and natural language processing to analyse complicated biological and clinical data in order to provide possibly personalised healthcare solutions. It promotes applications such as drug discovery, diagnostics, predictive analytics, and tailored treatment plans by integrating genomics, proteomics, and real-world data using AI capabilities with the goal of improving patient outcomes and accelerating innovation benefits for stakeholders ranging from pharmaceutical companies and healthcare providers to researchers.
Which region is expected to have the largest market share in the AI in precision medicine market?
The AI in precision medicine market is segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. North America is expected to hold the largest share of the AI in precision medicine market during the forecast period.
Which end-user segments have been included in the AI in precision medicine market report?
The report contains the following end-user segments:
  • Healthcare providers
  • Pharmaceutical & biotechnology companies
  • Medical device/equipment companies
  • Research centers, academic institutes, & government organizations
  • Others
How big is the global AI in precision medicine market today?
The global AI in precision medicine market is projected to grow from USD 0.78 billion in 2024 to USD 3.92 billion by 2030, at a CAGR of 30.7% from 2024–2030

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Table of Contents

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TITLE
PAGE NO
INTRODUCTION
1
RESEARCH METHODOLOGY
34
EXECUTIVE SUMMARY
67
PREMIUM INSIGHTS
89
MARKET OVERVIEW
91
  • 5.1 MARKET DYNAMICS
    DRIVERS
    RESTRAINTS
    OPPORTUNITIES
    CHALLENGES
  • 5.2 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES
  • 5.3 INDUSTRY TRENDS
  • 5.4 ECOSYSTEM ANALYSIS
  • 5.5 SUPPLY CHAIN ANALYSIS
  • 5.6 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - PREDICTIVE ANALYTICS
    - NEURAL NETWORKS
    - KNOWLEDGE GRAPHS
    - CELL AND GENE THERAPIES
    - AI-DRIVEN SINGLE CELL ANALYSIS
    COMPLEMENTARY TECHNOLOGY
    - HIGH-PERFORMANCE COMPUTING (HPC)
    - NEXT-GENERATION SEQUENCING
    - REAL-WORLD EVIDENCE/REAL-WORLD DATA
    - EHR INTEGRATION
    - DIGITAL HEALTH PLATFORMS
    ADJACENT TECHNOLOGIES
    - CLOUD COMPUTING
    - BLOCKCHAIN TECHNOLOGY
    - INTERNET OF THINGS (IOT) AND WEARABLES
    - ROBOTICS AND AUTOMATION
    - 3D PRINTING FOR PERSONALIZED IMPLANTS AND DEVICES
  • 5.7 REGULATORY LANDSCAPE
    REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
    REGULATORY ANALYSIS
    - NORTH AMERICA
    - EUROPE
    - ASIA PACIFIC
    - LATIN AMERICA
    - MIDDLE EAST & AFRICA
  • 5.8 PRICING ANALYSIS
    AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY DEPLOYMENT
    AVERAGE SELLING PRICE TREND, BY REGION
  • 5.9 PORTER’S FIVE FORCES ANALYSIS
  • 5.10 PATENT ANALYSIS
    PATENT PUBLICATION TRENDS FOR THE ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET
    INSIGHTS: JURISDICTION AND TOP APPLICANT ANALYSIS
  • 5.11 KEY STAKEHOLDERS AND BUYING CRITERIA
    KEY STAKEHOLDERS IN BUYING PROCESS
    BUYING CRITERIA
  • 5.12 END-USER ANALYSIS
    UNMET NEEDS
    END-USER EXPECTATIONS
  • 5.13 KEY CONFERENCES & EVENTS IN 2024-2025
  • 5.14 CASE STUDY ANALYSIS
  • 5.15 ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET: INVESTMENT AND FUNDING SCENARIO
  • 5.16 ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET: BUSINESS MODELS
  • 5.17 IMPACT OF AI/GEN AI IN THE ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY APPLICATION
115
  • 6.1 INTRODUCTION
  • 6.2 DRUG DISCOVERY & DEVELOPMENT
    DRUG DISCOVERY
    - UNDERSTANDING DISEASE
    - DRUG REPURPOSING (INCLUDING DRUG PRIORITIZATION)
    - DE NOVO DRUG DESIGN
    - DRUG OPTIMIZATION
    - SAFETY AND TOXICITY
    CLINICAL DEVELOPMENT
  • 6.3 DIAGNOSTICS & SCREENING
    RISK ASSESSMENT & PATIENT STRATIFICATION
    DISEASE SCREENING
    DISEASE DIAGNOSIS
    DISEASE PROGRESSION, STAGING, AND PROGNOSIS
  • 6.4 THERAPEUTICS
    THERAPY SELECTION & PLANNING
    THERAPY MONITORING
    POST TREATMENT SURVEILLANCE & FOLLOW-UP
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY THERAPEUTIC AREA
134
  • 7.1 INTRODUCTION
  • 7.2 ONCOLOGY
  • 7.3 RARE DISEASES
  • 7.4 INFECTIOUS DISEASES
  • 7.5 NEUROLOGY
  • 7.6 CARDIOLOGY
  • 7.7 HEMATOLOGY
  • 7.8 OTHERS (IMMUNOLOGY, GENETIC DISORDERS (EXCLUDING RARE GENETIC DISORDERS), PSYCHIATRIC DISORDERS, METABOLIC DISORDERS, PAIN MANAGEMENT, RESPIRATORY, DERMATOLOGY, GASTROENTEROLOGICAL DISORDERS, AND UROLOGICAL DISORDERS, AMONG OTHERS)
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY COMPONENT
156
  • 8.1 INTRODUCTION
  • 8.2 SOFTWARE
  • 8.3 SERVICES
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY TOOLS
189
  • 9.1 INTRODUCTION
  • 9.2 MACHINE LEARNING
    DEEP LEARNING
    - CONVOLUTIONAL NEURAL NETWORKS (CNN)
    - RECURRENT NEURAL NETWORKS (RNN)
    - GENERATIVE ADVERSARIAL NETWORKS (GAN)
    - GRAPH NEURAL NETWORKS (GNN)
    - OTHERS
    SUPERVISED LEARNING (SUPPORT VECTOR MACHINE, CLASSIFICATION & REGRESSION ALGORITHMS)
    REINFORCEMENT LEARNING (Q-LEARNING, DEEP Q-NETWORKS)
    UNSUPERVISED LEARNING (K-MEANS, DIMENSIONALITY REDUCTION)
    OTHER MACHINE LEARNING TECHNOLOGIES (SEMI-SUPERVISED LEARNING AND OTHERS)
  • 9.3 NATURAL LANGUAGE PROCESSING (NLP)
  • 9.4 CONTEXT-AWARE PROCESSING AND COMPUTING
  • 9.5 COMPUTER VISION
  • 9.6 IMAGE ANALYSIS (INCLUDING OPTICAL CHARACTER RECOGNITION)
  • 9.7 OTHERS (IF ANY)
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY DEPLOYMENT
201
  • 10.1 INTRODUCTION
  • 10.2 CLOUD-BASED MODEL
  • 10.3 ON-PREMISE MODEL
  • 10.4 HYBRID MODEL
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY END-USER
235
  • 11.1 INTRODUCTION
  • 11.2 HEALTHCARE PROVIDERS
    HOSPITALS & CLINICS
    SPECIALITY CENTERS
    DIAGNOSTIC LABORATORIES
    OTHERS
  • 11.3 PHARMACEUTICAL & BIOTECHNOLOGY COMPANIES
  • 11.4 MEDICAL DEVICE/EQUIPMENT COMPANIES
  • 11.5 RESEARCH CENTERS, ACADEMIC INSTITUTES, & GOVERNMENT ORGANIZATIONS
  • 11.6 OTHERS (IF ANY)
ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE MARKET, BY REGION
267
  • 12.1 INTRODUCTION
  • 12.2 NORTH AMERICA
    MACROECONOMIC OUTLOOK FOR NORTH AMERICA
    US
    CANADA
  • 12.3 EUROPE
    MACROECONOMIC OUTLOOK FOR EUROPE
    GERMANY
    FRANCE
    UK
    ITALY
    SPAIN
    REST OF EUROPE
  • 12.4 ASIA PACIFIC
    MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
    CHINA
    JAPAN
    INDIA
    REST OF ASIA PACIFIC
  • 12.5 LATIN AMERICA
    MACROECONOMIC OUTLOOK FOR LATIN AMERICA
    BRAZIL
    MEXICO
    REST OF LATIN AMERICA
  • 12.6 MIDDLE EAST & AFRICA
    MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA
    GCC COUNTRIES
    REST OF MIDDLE EAST & AFRICA
COMPETITIVE LANDSCAPE
287
  • 13.1 OVERVIEW
  • 13.2 STRATEGIES ADOPTED BY KEY PLAYERS
  • 13.3 REVENUE SHARE ANALYSIS OF TOP MARKET PLAYERS
  • 13.4 MARKET SHARE ANALYSIS
  • 13.5 BRAND/PRODUCT COMPARATIVE ANALYSIS
  • 13.6 VALUATION AND FINANCIAL METRICS OF KEY ARTIFICIAL INTELLIGENCE IN PRECISION MEDICINE VENDORS
  • 13.7 COMPANY EVALUATION MATRIX: KEY PLAYERS 2023
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT, KEY PLAYERS, 2023
    - COMPANY FOOTPRINT
    - REGION FOOTPRINT
    - APPLICATION FOOTPRINT
    - THERAPEUTIC AREA FOOTPRINT
    - COMPONENT FOOTPRINT
    - DEPLOYMENT FOOTPRINT
    - END-USER FOOTPRINT
  • 13.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
    - DETAILED LIST OF STARTUPS/SMES
    - COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
  • 13.9 COMPETITIVE SCENARIO AND TRENDS
    PRODUCT LAUNCHES
    DEALS
    OTHERS
COMPANY PROFILES
295
  • 14.1 KEY PLAYERS
    NVIDIA CORPORATION
    GOOGLE
    MICROSOFT
    IBM
    ILLUMINA, INC.
    EXSCIENTIA
    INSILICO MEDICINE INC.
    GE HEALTHCARE
    TEMPUS
    SIEMENS HEALTHINEERS
    BIOXCEL THERAPEUTICS, INC.
    BENEVOLENTAI
    PATHAI
    GUARDANT HEALTH
    GRAIL
    FOUNDATION MEDICINE, INC.
    FLATIRON HEALTH
    PROSCIA INC.
    VERGE GENOMICS
    DEEP GENOMICS
  • 14.2 OTHER PLAYERS
    PREDICTIVE ONCOLOGY, INC.
    PAIGE AI, INC.
    DENSITAS INC
    ZEPHYR AI
    PAIGE AI, INC.
APPENDIX
299
  • 15.1 DISCUSSION GUIDE
  • 15.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
  • 15.3 AVAILABLE CUSTOMIZATIONS
  • 15.4 RELATED REPORTS
  • 15.5 AUTHOR DETAILS

The study involved significant activities to estimate the current size of the AI in precision medicine market. Exhaustive secondary research was done to collect information on the AI in precision medicine market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain using primary research. Different approaches, such as top-down and bottom-up, were employed to estimate the total market size. After that, the market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the AI in precision medicine market.

Secondary Research

This research study involved the wide use of secondary sources, directories, and databases such as Dun & Bradstreet, Bloomberg Businessweek, and Factiva; white papers, annual reports, and companies’ house documents; investor presentations; and the SEC filings of companies. The market for the companies offering AI in precision medicine solutions is arrived at by secondary data available through paid and unpaid sources, analyzing the product portfolios of the major companies in the ecosystem, and rating the companies by their performance and quality. Various sources were referred to in the secondary research process to identify and collect information for this study. The secondary sources include annual reports, press releases, investor presentations of companies, white papers, journals, certified publications, and articles from recognized authors, directories, and databases.

Various secondary sources were referred to in the secondary research process to identify and collect information related to the study. These sources included annual reports, press releases, investor presentations of AI in precision medicine vendors, forums, certified publications, and whitepapers. The secondary research was used to obtain critical information on the industry’s value chain, the total pool of key players, market classification, and segmentation from the market and technology-oriented perspectives.

Primary Research

In the primary research process, various primary sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information for this report. The primary sources from the supply side included industry experts, such as Chief Executive Officers (CEOs), Vice Presidents (VPs), marketing directors, technology and innovation directors, and related key executives from various key companies and organizations operating in the AI in precision medicine market.

After the complete market engineering (calculations for market statistics, market breakdown, market size estimations, market forecasting, and data triangulation), extensive primary research was conducted to gather information and verify and validate the critical numbers arrived at. Primary research was also undertaken to identify the segmentation types, industry trends, competitive landscape of AI in precision medicine solutions offered by various market players, and key market dynamics, such as drivers, restraints, opportunities, challenges, industry trends, and key player strategies.

In the complete market engineering process, the top-down and bottom-up approaches and several data triangulation methods were extensively used to perform the market estimation and market forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to list the key information/insights throughout the report.

Breakdown of Primary Participants:

AI in Precision Medicine Market

Note 1: Others include sales managers, marketing managers, and product managers.

Note 2: Tiers of companies are defined based on their total revenues in 2023. Tier 1 = >USD 1 billion, Tier 2 = USD 500 million to USD 1 billion, and Tier 3 = < USD 500 million.

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Both top-down and bottom-up approaches were used to estimate and validate the total size of the AI in precision medicine market. These methods were also used extensively to estimate the size of various subsegments in the market. The research methodology used to estimate the market size includes the following:

  • The key players in the industry and markets have been identified through extensive secondary research.
  • The industry’s supply chain and market size, in terms of value, have been determined through primary and secondary research processes.
  • All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources.
AI in Precision Medicine Market

Data Triangulation

After arriving at the overall market size using the market size estimation processes explained above, the market was split into several segments and subsegments. The data triangulation and market breakup procedures were employed, wherever applicable, to complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment. The data was triangulated by studying various factors and trends from both the demand and supply sides.

Market Definition

The AI in precision medicine market utilises AI technologies such as machine learning and natural language processing to analyse complicated biological and clinical data in order to provide possibly personalised healthcare solutions. It promotes applications such as drug discovery, diagnostics, predictive analytics, and tailored treatment plans by integrating genomics, proteomics, and real-world data using AI capabilities with the goal of improving patient outcomes and accelerating innovation benefits for stakeholders ranging from pharmaceutical companies and healthcare providers to researchers.

Stakeholders

  • Transfection products manufacturing companies
  • Pharmaceutical & Biopharmaceutical Companies
  • Chemical Companies
  • Biopharmaceutical Companies
  • Contract Research Organizations (CROs)
  • Contract Development and Manufacturing Organizations (CDMOs)
  • Research Institutes and Universities 
  • Venture Capitalists & Investors
  • Government Associations

Report Objectives

  • To define, describe, and forecast the AI in precision medicine market based on application, therapeutic area, component, tools, deployment, end user, and region.
  • To provide detailed information about the major factors (drivers, opportunities, restraints, and challenges) influencing the growth of the market
  • To analyze opportunities for stakeholders by identifying the high-growth segments of the market
  • To forecast the size of the market segments with respect to five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To analyze subsegments of the market with respect to individual growth trends, prospects, and contributions to the overall market
  • To profile the key players and comprehensively analyze their market sizes and core competencies.
  • To track and analyze competitive developments such as acquisitions, collaborations, agreements, mergers, product launches & updates, partnerships, expansions, and other recent developments in the market globally.

Previous Versions of this Report

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Growth opportunities and latent adjacency in AI in Precision Medicine Market

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