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レポート概要
| 本調査レポートでは、世界の生命科学における人工知能市場について調査し、イントロダクション、調査手法、エグゼクティブサマリー、市場動向、用途別（創薬、医療診断、バイオテクノロジー、臨床試験、精密・個別化医療）分析、地域別分析、競争状況、投資分析、市場機会・将来動向など、以下の構成でお届けいたします。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場動向 ・世界の生命科学における人工知能市場規模：用途別（創薬、医療診断、バイオテクノロジー、臨床試験、精密・個別化医療） ・世界の生命科学における人工知能市場規模：地域別 ・競争状況 ・投資分析 ・市場機会・将来動向 |
The artificial intelligence in life sciences market was valued at USD 1,255.3 million in 2020, and it is estimated to be worth USD 3943.96 million by 2025, registering a CAGR of 21.1% during the forecast period (2021 – 2026). Most industries, including life sciences, are witnessing a transformation, owing to increasing cost pressure, a greater need for productivity, and disruption caused by new and innovative market players.
– An emerging area of artificial intelligence (AI), specifically the analysis of small systems-of-interest specific datasets, can be utilized to improve drug development and personalized medicine. According to a study published in Science Translational Medicine, Quadratic Phenotypic Optimization Platform (QPOP), an AI platform is capable of substantially improving combination therapy in bortezomib-resistant multiple myeloma, which is used to identify the best drug combinations for individual multiple myeloma patients.
– Furthermore, complex diseases, such as cancer, often require effective drug combinations to make any significant therapeutic impact. As the drugs in these combination therapies become increasingly specific to molecular targets, designing effective drug combinations, as well as choosing the right drug combination for the right patient, becomes more difficult. With the high average development costs (around USD 2 billion for a new approved treatment), low clinical trial drug success rate (below 12%), low return-on-investment (ROI) due to reduced healthcare expenditure, and focus on rare diseases, drug discovery is becoming more inefficient.
– Clinical trial research is an extensive progress, which can be reduced with the help of AI in numerous ways. One is by using advanced predictive analytics on a broad range of data to identify candidates for clinical trials for target populations more quickly. Additionally, machine learning applications can make clinical trials more efficient by facilitating tasks, such as calculating ideal sample sizes, facilitating patient recruitment, and using medical records to minimize data errors.
– Artificial Intelligence (AI) presents one of the most promising and potentially transformative opportunities for the life sciences industry. AI will be a key investment target in the coming years, with myriad organizations hoping to capitalize on its potential. The number of applications is expected to continue to increase and the investors are expected to enter the AI industry early.
– The COVID-19 outbreak would have profound and lasting impacts on the health and life sciences industry. The outbreak has caused life sciences organizations to adjust to supply chain and clinical development disruptions and financial challenges that would have previously been unthinkable. In the near term, healthcare organizations would accelerate innovation to respond to the crisis. These investments would enable healthcare organizations post-COVID-19 to rethink care delivery and financing.
Key Market Trends
Clinical Trials is Expected to Hold a Significant Market Share
– Clinical trials are one of the most data-intensive tasks in the life sciences industry. They generate vast sets of data everyday monitoring, several variables of a patient under observation. Subjecting these data sets to intelligent AI algorithms can help the researchers to screen meaningful correlation even between loosely coupled data.
– This is encouraging many pharmaceutical companies and clinical research organizations to invest in technologies, like artificial intelligence. In the current market scenario, the rapid adoption of AI is widely seen in the pharmaceutical sector, who are responsible for almost 50% of the clinical trials conducted globally every year.
– Furthermore, the increasing number of clinical trials all over the world is producing colossal amounts of data that is available in the public domain. Over the forecast period, these numbers are expected to increase by at least 11%, creating new opportunities for AI in clinical trial applications, especially in Europe and North America.
– Such rapid growth in clinical trials and public availability of data is helping and encouraging many research institutes to prefer AI over traditional software solutions so that they can obtain specific results from data available in the public domain without having the entire trial procedure. Additionally, many countries, like China, the United States, and several European countries, are making clinical trial data available in the public domain supporting the growth of AI solutions, globally.
The United States is Expected to Hold a Major Share in the Market
– The United States is the largest market for AI solutions in the life sciences market, owing to the high demand for AI solutions from almost all the life sciences applications in the United States. In addition, the significant presence of pharmaceutical companies and the global demand for US pharmaceutical exports are increasingly motivating companies to invest in R&D activities.
– According to the Pharmaceutical Research and Manufacturers of America (PhRMA), the Pharmaceutical Manufacturers Association, the pharmaceutical companies in the United States spent approximately USD 80 billion worldwide on R&D in 2018, and expenditure in the companies’ domestic market of the United States contributed an overwhelming share.
– PhRMA also revealed that, on average, most pharmaceutical companies in the country spent over 19% of their total revenues in R&D activities. Moreover, PhRMA members spent around 19.5% of their combined global revenues on R&D in 2018. This share increased to approximately 22% when only considering the sales and R&D expenditure from the domestic market of the United States.
– Many pharmaceuticals companies, such as Pfizer and Johnson & Johnson, and several clinical research organizations in the country, are increasingly spending on AI solutions, and according to ClinicalTrials.gov, it is projected that by 2024 Johnson & Johnson will have the largest research and development spending among pharmaceutical companies. Also, companies, like Pfizer, despite collaborating with several AI vendors, are investing in updating their in-house AI operations to support their ongoing several drug discovery and patient monitoring processes.
Competitive Landscape
Artificial intelligence in the life sciences market is highly competitive and consists of several major players. The market appears to be moderately concentrated. The major players with prominent shares in the market are focusing on expanding their customer base across foreign countries. These companies are leveraging strategic collaborative initiatives to increase their market share and increase their profitability. The companies operating in the market are also acquiring start-ups working on artificial intelligence in life sciences technologies to strengthen their product capabilities.
– Jun 2020 – The University of Kentucky announced a research collaboration with Atomwise, an industry leader in using artificial intelligence (AI) for small molecule discovery, to explore potential COVID-19 therapies.
– Mar 2020 – IBM Watson Health and EBSCO Information Services (EBSCO) announced a strategic collaboration aimed toward enhancing clinical decision support (CDS) and operations for healthcare providers and health systems. The companies are combining their respective solution suites DynaMed and IBM Micromedex with Watson into a single, global solution called “DynaMed and Micromedex with Watson.” The combined solution suite will be designed to bring together drug and disease content, into a single source for evidence-based insights to help inform clinical decisions.
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1 INTRODUCTION
1.1 Study Assumptions
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Rising Adoption of AI in the Field of R&D
4.2.2 High Emphasis on the Development of Precision Medicine and Personalized Drugs
4.2.3 Increasing Demand for AI in Drug Discovery
4.3 Market Restraints
4.3.1 High Initial Costs and Concerns over the Replacement of Human Workforce
4.4 Porters 5 Force Analysis
4.4.1 Threat of New Entrants
4.4.2 Bargaining Power of Buyers/Consumers
4.4.3 Bargaining Power of Suppliers
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
4.5 Technology Snapshot
4.6 Impact of COVID-19 on the Industry
5 MARKET SEGMENTATION
5.1 Application
5.1.1 Drug Discovery
5.1.2 Medical Diagnosis
5.1.3 Biotechnology
5.1.4 Clinical Trails
5.1.5 Precision and Personalized Medicine
5.1.6 Patient Monitoring
5.2 Geography
5.2.1 North America
5.2.1.1 United States
5.2.1.2 Canada
5.2.2 Europe
5.2.2.1 Germany
5.2.2.2 United Kingdom
5.2.2.3 France
5.2.2.4 Rest of Europe
5.2.3 Asia-Pacific
5.2.3.1 China
5.2.3.2 Japan
5.2.3.3 India
5.2.3.4 South Korea
5.2.3.5 Rest of Asia-Pacific
5.2.4 Rest of the World
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 IBM Corporation
6.1.2 NuMedii Inc.
6.1.3 Atomwise Inc
6.1.4 AiCure LLC
6.1.5 Nuance Communications Inc.
6.1.6 Sensely Inc.
6.1.7 Sophia Genetics SA
6.1.8 Insilico Medicine Inc.
6.1.9 Enlitic Inc.
6.1.10 APIXIO Inc.
6.1.11 Zebra Medical Vision
6.1.12 twoXAR Inc.
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS