Summary

Global AI In Life Science Market was valued at USD 12.43 Billion in 2024 and is expected to reach USD 29.30 Billion by 2030 with a CAGR of 15.32% during the forecast period. The Global AI in Life Science Market is being driven by the increasing demand for faster, more accurate drug discovery, personalized medicine, and optimized clinical trials. AI technologies enable life science companies to analyze vast datasets, accelerating the identification of potential drug candidates and improving research efficiency. AI's ability to uncover hidden patterns in genomic, clinical, and medical data also facilitates the development of personalized treatment plans, enhancing patient outcomes. AI-driven automation is transforming research and development workflows, reducing costs and time-to-market for new therapies. The rise in chronic diseases and an aging global population further amplify the need for innovative solutions. With continuous advancements in machine learning and data analytics, AI is playing a crucial role in revolutionizing the life sciences industry.
Key Market Drivers
Increasing Demand for Personalized Medicine
The growing need for personalized medicine is one of the primary drivers of the Global AI in Life Science Market. As healthcare continues to shift towards more individualized treatment approaches, AI plays a pivotal role in enabling this transformation. Personalized medicine tailors medical treatment to the specific characteristics of each patient, such as their genetic profile, lifestyle, and environment. This approach ensures that patients receive therapies that are most likely to be effective for their unique conditions, minimizing the trial-and-error process that has traditionally characterized medical treatments.
AI is integral to this process because it can analyze and interpret vast amounts of medical data far more efficiently than human researchers. This includes genomic, clinical, and lifestyle data, which AI can use to identify patterns and correlations that would be difficult for clinicians to detect manually. For example, AI models can uncover genetic markers linked to specific diseases or predict how a patient’s body will respond to a particular drug based on their unique genetic makeup. AI can also optimize clinical trial design by identifying suitable candidates based on their genetic profiles and disease characteristics, thus improving the precision and speed of clinical trials.
As the demand for personalized medicine increases, AI’s ability to analyze complex data and provide insights for more targeted treatments becomes even more crucial. This technology not only enables more effective and efficient healthcare but also holds the potential to significantly reduce healthcare costs, minimize adverse drug reactions, and improve overall patient outcomes. With continuous advancements in AI algorithms and data analytics, personalized medicine is expected to become more mainstream, propelling the growth of the AI in life sciences market.
AI for Precision Diagnostics and Imaging
Precision diagnostics and imaging are becoming more sophisticated with the integration of AI technologies, driving the Global AI in Life Science Market further. AI algorithms are increasingly being used to enhance diagnostic accuracy, particularly in medical imaging, which is a cornerstone of precision medicine. AI’s ability to analyze medical images—such as MRI scans, CT scans, and X-rays—allows healthcare providers to detect diseases early and accurately, often identifying issues that may be missed by human radiologists. In April 2024, IQVIA and Salesforce expanded their global partnership to accelerate the development of the Life Sciences Cloud. This collaboration integrates IQVIA's OCE innovations with Salesforce’s platform, providing customers with a comprehensive engagement solution. By leveraging IQVIA's data, expertise, and advanced analytics, Salesforce's CRM software aims to transform healthcare professional (HCP) and patient interactions, delivering a more seamless and effective experience.
In medical imaging, AI-driven tools are capable of automating image analysis, detecting anomalies such as tumors, plaques, and lesions, and offering insights that guide clinicians toward the most effective treatment plans. AI also plays a critical role in early disease detection, particularly in fields like oncology, cardiology, and neurology. AI algorithms can analyze vast amounts of imaging data to detect patterns that indicate the presence of diseases such as cancer or heart disease at an early stage when treatment options are more effective and less invasive.
AI-based diagnostic tools are not only improving accuracy but also increasing efficiency by automating time-consuming tasks. For example, in radiology, AI tools can quickly analyze large volumes of imaging data, allowing radiologists to focus on complex cases and improving workflow efficiency. This ability to provide fast and accurate diagnostics is transforming the medical industry, enhancing patient outcomes, and reducing healthcare costs.
Increasing Demand for Healthcare Automation
The demand for healthcare automation is rising rapidly, and AI is a key enabler in this transformation. Automation in the life sciences industry aims to streamline administrative tasks, clinical workflows, and research processes, ultimately reducing operational costs and improving overall efficiency. AI-driven automation tools are already being utilized to handle routine tasks such as patient data entry, medical record management, appointment scheduling, and even regulatory compliance monitoring. In June 2024, Medidata introduced Clinical Data Studio, a unified platform that consolidates data from both Medidata and non-Medidata sources. This technology empowers stakeholders to monitor data quality, accelerate trial processes, and make faster, safer decisions, while supporting comprehensive data and risk management strategies. AI plays a key role in identifying potential issues and safety signals.
In laboratory settings, AI is being used to automate processes like high-throughput screening, data collection, and analysis. These automation solutions not only save time but also reduce human errors, ensuring more consistent and accurate results. AI in automation helps address the shortage of skilled professionals in various healthcare fields by enabling machines to take over repetitive and time-consuming tasks, allowing human workers to focus on higher-value tasks like patient care and research.
As healthcare systems around the world face increasing pressure to deliver better services at lower costs, the role of AI in automation will continue to expand, offering life science companies a pathway to more efficient and cost-effective operations.
Expanding Applications in Genomics and Precision Medicine
The expanding applications of AI in genomics are significantly contributing to the growth of the Global AI in Life Science Market. Genomics, the study of genes and their functions, is at the forefront of personalized medicine. AI technologies are being leveraged to analyze vast amounts of genomic data and uncover insights that were previously beyond reach. This ability to process large-scale genomic datasets quickly and accurately is revolutionizing drug discovery, disease diagnosis, and treatment planning.
AI algorithms can identify genetic variations associated with diseases, predict the likelihood of developing certain conditions, and even recommend personalized treatment options based on a patient’s genetic makeup. AI is particularly beneficial in processing next-generation sequencing (NGS) data, which generates enormous amounts of genetic information that would be too complex for traditional computational methods to analyze. By harnessing AI’s capabilities, researchers can identify novel biomarkers for diseases, which is critical for early detection and the development of targeted therapies.
The synergy between AI and genomics is paving the way for more effective and precise treatments in oncology, cardiology, and neurology. As AI continues to advance, its applications in genomics will likely expand, further driving innovation in precision medicine and fueling the growth of the life science AI market.
Rising Chronic Disease Burden
The global increase in chronic diseases is another major driver of the AI in Life Science Market. Conditions such as diabetes, cardiovascular disease, cancer, and neurological disorders are becoming more prevalent due to an aging population and lifestyle changes. AI is increasingly being deployed to aid in the management and treatment of these diseases, offering solutions to improve patient outcomes and reduce healthcare costs. According to WHO, in 2021, noncommunicable diseases (NCDs) were responsible for at least 43 million deaths, representing 75% of all non-pandemic-related fatalities globally. Of these, 18 million individuals died from an NCD before reaching the age of 70, with 82% of these premature deaths occurring in low- and middle-income countries. Overall, 73% of all NCD-related deaths take place in these countries. Cardiovascular diseases lead as the primary cause of NCD-related fatalities, accounting for at least 19 million deaths in 2021. This is followed by cancers, with 10 million deaths, chronic respiratory diseases, which caused 4 million deaths, and diabetes, responsible for over 2 million deaths, including those linked to kidney disease caused by diabetes.
AI tools can analyze medical data to detect early signs of chronic diseases, predict disease progression, and recommend personalized treatment strategies. In cardiology, for example, AI is used to assess heart function and detect signs of heart disease, while in oncology, AI is used to identify early-stage tumors and suggest treatment options. AI is also instrumental in managing chronic diseases by monitoring patients in real-time, helping clinicians adjust treatment plans based on ongoing data and preventing complications.
As the burden of chronic diseases grows, AI’s ability to provide predictive analytics and personalized care will become increasingly valuable, supporting better disease management and improving quality of life for patients.
Growing Investment in Life Sciences and Healthcare Innovation
Increased investment in life sciences and healthcare innovation is a significant driver of AI’s growth in the industry. Venture capitalists, private equity firms, and government organizations are pouring substantial funds into life sciences research, technology development, and healthcare innovation. This investment is fueling the development of AI solutions tailored to specific healthcare challenges, such as drug discovery, diagnostics, and patient care. In March 2024, Google Cloud launched Vertex AI Search for Healthcare, designed to assist healthcare and life sciences organizations in enhancing interoperability, establishing a stronger data foundation, and utilizing generative AI tools to improve patient outcomes.
With AI becoming an integral part of life science R&D, these investments are accelerating the development of AI-driven tools and platforms that can solve real-world healthcare problems. As the demand for more efficient and cost-effective healthcare solutions rises, investment in AI technologies is expected to continue expanding, further driving the growth of AI in life sciences.
Key Market Challenges
Lack of Standardization
Lack of standardization in data formats, clinical practices, and AI algorithms is another challenge hindering the growth of the Global AI in Life Science Market. The healthcare and life sciences industries are complex and fragmented, with various stakeholders using different systems, platforms, and protocols. This lack of standardization makes it difficult for AI systems to integrate seamlessly into existing healthcare infrastructures and analyze data from multiple sources. The inconsistency in how data is collected, stored, and shared between healthcare providers, research institutions, and AI companies can result in suboptimal performance of AI systems and reduced trust in their outcomes.
There are currently no universal standards for evaluating the effectiveness and reliability of AI tools in life sciences. The lack of common benchmarks and evaluation protocols can slow down the adoption of AI, as healthcare providers and regulatory bodies may be uncertain about the performance of AI-based solutions. For AI to become more effective in the life sciences market, it is essential to develop standardized data formats, protocols, and AI model evaluation frameworks. Creating a more cohesive and standardized ecosystem will facilitate smoother integration of AI technologies and improve their efficiency and impact across the healthcare industry.
High Costs of Implementation and Integration
The high costs of implementing and integrating AI solutions into existing healthcare and life science systems present a significant barrier for many organizations. While AI has the potential to drive efficiency and reduce long-term costs, the initial investment required for purchasing AI tools, training staff, and ensuring system compatibility can be prohibitive. This includes the costs of procuring high-quality datasets, setting up advanced computing infrastructure, and ensuring compliance with regulatory requirements.
Smaller healthcare institutions or those in resource-limited settings may find it difficult to justify the investment in AI technologies, especially when the return on investment is not immediately apparent. Integrating AI into existing healthcare workflows can be challenging, as it requires significant changes to operational processes, infrastructure, and even organizational culture. Healthcare providers need to assess the long-term benefits of AI implementation carefully, weighing the initial costs against the potential for improved patient outcomes, streamlined workflows, and reduced errors. Financial constraints could be especially challenging for hospitals or clinics in emerging markets or regions with underfunded healthcare systems, potentially slowing the adoption of AI in life sciences on a global scale.
Key Market Trends
Advancements in Drug Discovery and Development
AI is revolutionizing the field of drug discovery and development, acting as a major catalyst for the Global AI in Life Science Market. Traditionally, the drug discovery process is long, expensive, and fraught with uncertainty. It often takes years of research, testing, and regulatory approval before a drug reaches the market, with many candidates failing along the way. AI addresses these challenges by enabling faster and more accurate identification of potential drug candidates, thereby reducing time-to-market and the overall cost of development. In October 2023, Fujitsu Limited and RIKEN's HPC- and AI-driven Drug Development Platform Division unveiled an AI technology capable of predicting protein structural changes from electron microscope images, utilizing generative AI to create 3D density maps.
Deep learning, machine learning, and natural language processing (NLP) algorithms are particularly effective in drug discovery. These AI tools can rapidly analyze enormous datasets, including chemical libraries, clinical trial data, and molecular structures, to predict which compounds have the highest likelihood of success in treating specific diseases. By doing so, AI models can prioritize the most promising candidates for further testing, reducing the time and resources spent on unviable options. For example, AI can be used to design novel molecules with higher binding affinity to target proteins or enzymes involved in disease processes, accelerating the identification of potential drug candidates.
AI can improve the design of clinical trials by helping researchers select the most appropriate patient populations, identify biomarkers for monitoring treatment response, and predict potential side effects. This ability to use AI for data-driven decision-making not only improves the efficacy of drug discovery but also enhances the safety profile of new treatments. As AI technologies continue to advance, they will further streamline the drug development process, making it more cost-effective and efficient while facilitating the creation of innovative therapies for complex diseases like cancer, Alzheimer’s, and cardiovascular disorders.
Enhancing Clinical Trials and Patient Recruitment
AI is playing an increasingly crucial role in enhancing the efficiency and effectiveness of clinical trials, a vital aspect of the Global AI in Life Science Market. Clinical trials are essential for testing the safety and efficacy of new treatments, but they can be expensive and time-consuming due to the complexity of patient recruitment and trial design. AI offers several advantages in streamlining these processes and improving trial outcomes.
One of the main ways AI is transforming clinical trials is by optimizing patient recruitment. AI algorithms can analyze patient data from electronic health records (EHRs), clinical databases, and genetic profiles to identify individuals who meet specific trial eligibility criteria. This targeted approach helps researchers find suitable candidates more quickly and accurately, reducing recruitment time and increasing the likelihood of enrolling patients who will respond well to the treatment being tested. AI can help predict patient enrollment rates, identify potential dropouts, and recommend adjustments to recruitment strategies based on real-time data.
AI also enhances trial design by providing insights into optimal dosing regimens, monitoring biomarkers for treatment efficacy, and predicting possible side effects. With the help of machine learning models, researchers can simulate and model clinical trial outcomes before implementation, reducing the need for trial-and-error testing and improving the chances of success. AI-powered platforms enable real-time monitoring of patients during trials, allowing researchers to track treatment responses, side effects, and overall health outcomes more effectively. This leads to more efficient trials and faster time-to-market for new treatments.
Segmental Insights
Offering Insights
Based on the Offering, Software was dominating the Global AI in Life Science Market. The rapid advancement of artificial intelligence technologies in the life sciences sector has primarily been driven by software solutions. AI-powered software tools enable life science organizations to harness vast amounts of complex biological, clinical, and genomic data, which is central to making significant advancements in areas such as drug discovery, precision medicine, and diagnostics. Software solutions, particularly those utilizing machine learning (ML), deep learning (DL), and natural language processing (NLP), are being increasingly deployed across the life sciences value chain. For instance, AI algorithms are used to analyze large datasets, identify biomarkers, and model drug interactions, significantly accelerating the drug discovery process. These AI tools allow researchers to predict which molecules might be most effective in treating diseases, cutting down on the time and costs traditionally required for research and development. AI-powered software is instrumental in advancing precision medicine, enabling personalized treatment plans based on individual genetic profiles and clinical data.
In addition to drug discovery and precision medicine, AI software is transforming diagnostics and imaging. AI-driven diagnostic platforms analyze medical images like MRIs, CT scans, and X-rays to detect abnormalities such as tumors, fractures, and lesions. This software has shown great potential in improving diagnostic accuracy, reducing human error, and enabling early disease detection, leading to better patient outcomes. AI software is integral in clinical trial optimization, from patient recruitment to monitoring treatment responses, ensuring more efficient trials and quicker approvals for new therapies. One of the key advantages of AI software in the life sciences sector is its scalability. Software solutions can be easily adapted and deployed across various domains within the healthcare industry, making it accessible to a wide range of organizations, from research institutions and pharmaceutical companies to hospitals and diagnostic centers. The flexibility of AI software means it can be tailored to meet the specific needs of different stakeholders, making it a crucial element of AI adoption in life sciences.
Application Insights
Based on the Application segment, Drug Discovery was dominating the Global AI in Life Science Market. The growing need for faster, more cost-effective, and efficient drug development has driven the widespread adoption of AI technologies in this sector. Traditionally, drug discovery is a time-consuming and expensive process, often taking years for researchers to identify, test, and bring a new drug to market. AI has significantly transformed this process by enabling more efficient data analysis, improving predictive models, and reducing the reliance on trial-and-error testing. AI-driven drug discovery utilizes machine learning (ML) and deep learning (DL) algorithms to analyze vast datasets, including genetic information, molecular structures, clinical trial data, and chemical libraries. This enables researchers to identify potential drug candidates faster and with greater accuracy. One of the most significant advantages of AI in drug discovery is its ability to predict how molecules will interact with specific proteins or enzymes in the body, an essential component of developing new therapies. AI algorithms can also help optimize the drug design process by analyzing the chemical properties of compounds and predicting their efficacy and toxicity before moving to the experimental stages.
AI accelerates the process of drug repurposing, where existing drugs are tested for efficacy in treating different diseases. This has been particularly useful in responding to urgent health crises, such as the COVID-19 pandemic, where AI models were employed to identify existing drugs that could potentially be repurposed for treating the virus. In this context, AI has proven invaluable for rapidly analyzing large datasets, identifying promising candidates, and guiding the design of new therapeutic strategies. The role of AI in drug discovery extends beyond simply identifying drug candidates. AI is also crucial in optimizing the drug development pipeline, improving clinical trial efficiency, and minimizing risks. By analyzing data from previous trials, AI can help design more effective trials, select suitable patient cohorts, and predict potential side effects. AI tools can even help monitor patient responses during trials, ensuring better patient safety and more reliable data. The increased efficiency brought about by AI leads to reduced time-to-market for new drugs, which is a significant advantage in the competitive pharmaceutical industry.
Regional Insights
North America was dominating the Global AI in Life Science Market. The region’s strong presence in the life sciences industry, combined with its advanced technological infrastructure and extensive research capabilities, has made it the leading hub for the adoption and development of AI technologies in life sciences. North America, particularly the United States, is home to many of the world’s largest pharmaceutical companies, biotechnology firms, and research institutions, all of which are leveraging AI to enhance drug discovery, medical diagnostics, and personalized medicine.
The significant investments in AI research and development (R&D) by both private companies and government agencies have been a driving force behind North America's dominance in this market. Organizations such as National Institutes of Health (NIH) and the U.S. Food and Drug Administration (FDA) have been instrumental in facilitating AI adoption in the life sciences sector by creating regulatory frameworks and funding initiatives aimed at advancing AI applications in healthcare. Major pharmaceutical companies, like Pfizer, Johnson & Johnson, and Merck, have invested heavily in AI-driven solutions to accelerate drug discovery, optimize clinical trials, and improve treatment outcomes, further solidifying North America's leadership position.
North America has a highly favorable environment for AI-driven innovation, with abundant venture capital funding supporting AI startups focused on life sciences. The region is home to leading AI technology companies, including IBM Watson Health, Google Health, and Microsoft, which are heavily involved in AI healthcare and life science applications. The collaboration between tech companies and life sciences firms has facilitated the development of sophisticated AI software and platforms that analyze big data, enhance decision-making, and improve patient care. The region’s well-established healthcare infrastructure also supports the integration of AI solutions, with hospitals, clinics, and diagnostic centers increasingly adopting AI tools for medical imaging, diagnostics, and personalized treatment planning.
Key Market Players
• IBM Corporation
• Atomwise Inc.
• Nuance Communications, Inc.
• NuMedii, Inc.
• AiCure LLC
• Apixio Holdings, LLC
• Insilico Medicine
• Enlitic, Inc.
• Sensely, Inc.
• SINEQUA Group
Report Scope:
In this report, the Global AI In Life Science Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
• AI In Life Science Market, By Offering:
o Software
o Hardware
o Services
• AI In Life Science Market, By Deployment:
o On-Premises
o Cloud
• AI In Life Science Market, By Application:
o Medical Diagnosis
o Drug Discovery
o Precision & Personalized Medicine
o Biotechnology
o Clinical Trials
o Patent Monitoring
• AI In Life Science Market, By Region:
o North America
§ United States
§ Canada
§ Mexico
o Europe
§ France
§ United Kingdom
§ Italy
§ Germany
§ Spain
o Asia-Pacific
§ China
§ India
§ Japan
§ Australia
§ South Korea
o South America
§ Brazil
§ Argentina
§ Colombia
o Middle East & Africa
§ South Africa
§ Saudi Arabia
§ UAE
Competitive Landscape
Company Profiles: Detailed analysis of the major companies present in the Global AI In Life Science Market.
Available Customizations:
Global AI In Life Science market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:
Company Information
• Detailed analysis and profiling of additional market players (up to five).

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Table of Contents

1. Product Overview
1.1. Market Definition
1.2. Scope of the Market
1.2.1. Markets Covered
1.2.2. Years Considered for Study
1.2.3. Key Market Segmentations
2. Research Methodology
2.1. Objective of the Study
2.2. Baseline Methodology
2.3. Key Industry Partners
2.4. Major Association and Secondary Sources
2.5. Forecasting Methodology
2.6. Data Triangulation & Validations
2.7. Assumptions and Limitations
3. Executive Summary
3.1. Overview of the Market
3.2. Overview of Key Market Segmentations
3.3. Overview of Key Market Players
3.4. Overview of Key Regions/Countries
3.5. Overview of Market Drivers, Challenges, Trends
4. Voice of Customer
5. Global AI in Life Science Market Outlook
5.1. Market Size & Forecast
5.1.1. By Value
5.2. Market Share & Forecast
5.2.1. By Offering (Software, Hardware, and Services)
5.2.2. By Deployment (On-Premises and Cloud)
5.2.3. By Application (Medical Diagnosis, Drug Discovery, Precision & Personalized Medicine, Biotechnology, Clinical Trials, and Patent Monitoring)
5.2.4. By Region
5.2.5. By Company (2024)
5.3. Market Map
6. North America AI in Life Science Market Outlook
6.1. Market Size & Forecast
6.1.1. By Value
6.2. Market Share & Forecast
6.2.1. By Offering
6.2.2. By Deployment
6.2.3. By Application
6.2.4. By Country
6.3. North America: Country Analysis
6.3.1. United States AI in Life Science Market Outlook
6.3.1.1. Market Size & Forecast
6.3.1.1.1. By Value
6.3.1.2. Market Share & Forecast
6.3.1.2.1. By Offering
6.3.1.2.2. By Deployment
6.3.1.2.3. By Application
6.3.2. Canada AI in Life Science Market Outlook
6.3.2.1. Market Size & Forecast
6.3.2.1.1. By Value
6.3.2.2. Market Share & Forecast
6.3.2.2.1. By Offering
6.3.2.2.2. By Deployment
6.3.2.2.3. By Application
6.3.3. Mexico AI in Life Science Market Outlook
6.3.3.1. Market Size & Forecast
6.3.3.1.1. By Value
6.3.3.2. Market Share & Forecast
6.3.3.2.1. By Offering
6.3.3.2.2. By Deployment
6.3.3.2.3. By Application
7. Europe AI in Life Science Market Outlook
7.1. Market Size & Forecast
7.1.1. By Value
7.2. Market Share & Forecast
7.2.1. By Offering
7.2.2. By Deployment
7.2.3. By Application
7.2.4. By Country
7.3. Europe: Country Analysis
7.3.1. Germany AI in Life Science Market Outlook
7.3.1.1. Market Size & Forecast
7.3.1.1.1. By Value
7.3.1.2. Market Share & Forecast
7.3.1.2.1. By Offering
7.3.1.2.2. By Deployment
7.3.1.2.3. By Application
7.3.2. United Kingdom AI in Life Science Market Outlook
7.3.2.1. Market Size & Forecast
7.3.2.1.1. By Value
7.3.2.2. Market Share & Forecast
7.3.2.2.1. By Offering
7.3.2.2.2. By Deployment
7.3.2.2.3. By Application
7.3.3. Italy AI in Life Science Market Outlook
7.3.3.1. Market Size & Forecast
7.3.3.1.1. By Value
7.3.3.2. Market Share & Forecast
7.3.3.2.1. By Offering
7.3.3.2.2. By Deployment
7.3.3.2.3. By Application
7.3.4. France AI in Life Science Market Outlook
7.3.4.1. Market Size & Forecast
7.3.4.1.1. By Value
7.3.4.2. Market Share & Forecast
7.3.4.2.1. By Offering
7.3.4.2.2. By Deployment
7.3.4.2.3. By Application
7.3.5. Spain AI in Life Science Market Outlook
7.3.5.1. Market Size & Forecast
7.3.5.1.1. By Value
7.3.5.2. Market Share & Forecast
7.3.5.2.1. By Offering
7.3.5.2.2. By Deployment
7.3.5.2.3. By Application
8. Asia-Pacific AI in Life Science Market Outlook
8.1. Market Size & Forecast
8.1.1. By Value
8.2. Market Share & Forecast
8.2.1. By Offering
8.2.2. By Deployment
8.2.3. By Application
8.2.4. By Country
8.3. Asia-Pacific: Country Analysis
8.3.1. China AI in Life Science Market Outlook
8.3.1.1. Market Size & Forecast
8.3.1.1.1. By Value
8.3.1.2. Market Share & Forecast
8.3.1.2.1. By Offering
8.3.1.2.2. By Deployment
8.3.1.2.3. By Application
8.3.2. India AI in Life Science Market Outlook
8.3.2.1. Market Size & Forecast
8.3.2.1.1. By Value
8.3.2.2. Market Share & Forecast
8.3.2.2.1. By Offering
8.3.2.2.2. By Deployment
8.3.2.2.3. By Application
8.3.3. Japan AI in Life Science Market Outlook
8.3.3.1. Market Size & Forecast
8.3.3.1.1. By Value
8.3.3.2. Market Share & Forecast
8.3.3.2.1. By Offering
8.3.3.2.2. By Deployment
8.3.3.2.3. By Application
8.3.4. South Korea AI in Life Science Market Outlook
8.3.4.1. Market Size & Forecast
8.3.4.1.1. By Value
8.3.4.2. Market Share & Forecast
8.3.4.2.1. By Offering
8.3.4.2.2. By Deployment
8.3.4.2.3. By Application
8.3.5. Australia AI in Life Science Market Outlook
8.3.5.1. Market Size & Forecast
8.3.5.1.1. By Value
8.3.5.2. Market Share & Forecast
8.3.5.2.1. By Offering
8.3.5.2.2. By Deployment
8.3.5.2.3. By Application
9. South America AI in Life Science Market Outlook
9.1. Market Size & Forecast
9.1.1. By Value
9.2. Market Share & Forecast
9.2.1. By Offering
9.2.2. By Deployment
9.2.3. By Application
9.2.4. By Country
9.3. South America: Country Analysis
9.3.1. Brazil AI in Life Science Market Outlook
9.3.1.1. Market Size & Forecast
9.3.1.1.1. By Value
9.3.1.2. Market Share & Forecast
9.3.1.2.1. By Offering
9.3.1.2.2. By Deployment
9.3.1.2.3. By Application
9.3.2. Argentina AI in Life Science Market Outlook
9.3.2.1. Market Size & Forecast
9.3.2.1.1. By Value
9.3.2.2. Market Share & Forecast
9.3.2.2.1. By Offering
9.3.2.2.2. By Deployment
9.3.2.2.3. By Application
9.3.3. Colombia AI in Life Science Market Outlook
9.3.3.1. Market Size & Forecast
9.3.3.1.1. By Value
9.3.3.2. Market Share & Forecast
9.3.3.2.1. By Offering
9.3.3.2.2. By Deployment
9.3.3.2.3. By Application
10. Middle East and Africa AI in Life Science Market Outlook
10.1. Market Size & Forecast
10.1.1. By Value
10.2. Market Share & Forecast
10.2.1. By Offering
10.2.2. By Deployment
10.2.3. By Application
10.2.4. By Country
10.3. MEA: Country Analysis
10.3.1. South Africa AI in Life Science Market Outlook
10.3.1.1. Market Size & Forecast
10.3.1.1.1. By Value
10.3.1.2. Market Share & Forecast
10.3.1.2.1. By Offering
10.3.1.2.2. By Deployment
10.3.1.2.3. By Application
10.3.2. Saudi Arabia AI in Life Science Market Outlook
10.3.2.1. Market Size & Forecast
10.3.2.1.1. By Value
10.3.2.2. Market Share & Forecast
10.3.2.2.1. By Offering
10.3.2.2.2. By Deployment
10.3.2.2.3. By Application
10.3.3. UAE AI in Life Science Market Outlook
10.3.3.1. Market Size & Forecast
10.3.3.1.1. By Value
10.3.3.2. Market Share & Forecast
10.3.3.2.1. By Offering
10.3.3.2.2. By Deployment
10.3.3.2.3. By Application
11. Market Dynamics
11.1. Drivers
11.2. Challenges
12. Market Trends & Developments
12.1. Merger & Acquisition (If Any)
12.2. Product Launches (If Any)
12.3. Recent Developments
13. Porter’s Five Forces Analysis
13.1. Competition in the Industry
13.2. Potential of New Entrants
13.3. Power of Suppliers
13.4. Power of Customers
13.5. Threat of Substitute Products
14. Competitive Landscape
14.1. IBM Corporation
14.1.1. Business Overview
14.1.2. Company Snapshot
14.1.3. Products & Services
14.1.4. Financials (As Reported)
14.1.5. Recent Developments
14.1.6. Key Personnel Details
14.1.7. SWOT Analysis
14.2. Atomwise Inc.
14.3. Nuance Communications, Inc.
14.4. NuMedii, Inc.
14.5. AiCure LLC
14.6. Apixio Holdings, LLC
14.7. Insilico Medicine
14.8. Enlitic, Inc.
14.9. Sensely, Inc.
14.10. SINEQUA Group
15. Strategic Recommendations
16. About Us & Disclaimer