Dublin, March 16, 2023 (GLOBE NEWSWIRE) -- The "Deep Learning Market in Drug Discovery and Diagnostics: Distribution by Therapeutic Areas and Key Geographical Regions: Industry Trends and Global Forecasts (2nd Edition), 2023-2035" report has been added to ResearchAndMarkets.com's offering.
This report features an extensive study of the current market landscape and the likely future potential of the deep learning solutions market within the healthcare domain. The report highlights the efforts of several stakeholders engaged in this rapidly emerging segment of the pharmaceutical industry. The report answers many key questions related to this domain.
Since the mid-twentieth century, computing devices have continually been explored for applications beyond mere calculations, to emerge as machines that possess intelligence. These targeted efforts have contributed to the introduction of artificial intelligence, the next-generation simulator that employs programmed machines possessing the ability to comprehend data and execute the instructed tasks.
The progress of artificial intelligence can be attributed to machine learning, a field of study imparting computers with the ability to think without being explicitly programmed. Deep learning is a complex machine learning algorithm that uses a neural network of interconnected nodes / neurons in a multi-layered structure, thereby enabling the interpretation of large volumes of unstructured data to generate valuable insights. The mechanism of this technique resembles the interpretation ability of human beings, making it a promising approach for big data analysis.
Owing to the distinct characteristic of deep learning algorithm to imitate human brain, it is currently being deployed in the life sciences domain, primarily for the purpose of drug discovery and diagnostics. Considering the challenges associated with drug discovery and development, such as the high attrition rate and increased financial burden, deep learning has been found to improve the overall R&D productivity and enhance diagnosis / prediction accuracy.
Recent advancements in the deep learning domain have demonstrated its potential in other healthcare-associated segments, such as medical image analysis, molecular profiling, virtual screening and sequencing data analysis. Driven by the ongoing pace of innovation and the profound impact of implementation of such solutions, deep learning is anticipated to witness substantial growth in the foreseen future.
Key Market Insights
What is the Current Market Landscape of the Deep Learning Market Focused on Drug Discovery and Diagnostics?
Currently, more than 200 industry players are focused on providing deep learning-based services / technologies for drug discovery and diagnostic purposes. The primary focus areas of these companies include big data analysis, medical imaging, medical diagnosis and genetic / molecular data analysis.
Further, these players are engaged in offering services across a wide range of therapeutic areas. It is worth highlighting that deep learning-powered diagnostic service providers offer various diagnostic solutions, such as structured analysis reports, image interpretation and biomarker identification solutions, with input data from several compatible devices.
What is the Market Size of Deep Learning in Drug Discovery?
Lately, the industry has witnessed the development of advanced deep learning technologies / software. These technologies possess the ability to obviate the concerns associated with the conventional drug discovery process. Eventually, such technologies will aid in the reduction of financial burden associated with drug discovery.
The global deep learning market focusing on drug discovery is anticipated to grow at a CAGR of over 20% between 2023 and 2035. By 2035, the deep learning in drug discovery market for oncological disorders is expected to capture the majority share. In terms of geography, the market in North America and Europe is anticipated to grow at a relatively faster pace by 2035.
What is the Market Size of Deep Learning in Diagnostics Market?
The adoption of deep learning-powered technologies to assist medical diagnosis, as well as prevention of diseases, has increased in the recent past. The global deep learning market focusing on diagnostics is anticipated to grow at a CAGR of over 15% between 2023 and 2035. By 2035, the deep learning in diagnostics market in North America is expected to capture the majority share. In terms of therapeutic areas, the deep learning in diagnostics market for endocrine and respiratory disorders is anticipated to grow at a relatively faster pace by 2035.
Which Segment held the Largest Share in Deep Learning Market?
The study covers the revenues from deep learning technology for their potential applications in the drug discovery and diagnostics domain. As of 2022, deep learning based diagnostics held the largest share of the market, owing to the efficiency and precision of applying deep learning-powered diagnostic solutions.
Further, the deep learning in drug discovery market is anticipated to grow at a relatively higher growth rate during the given time period with several pharmaceutical companies actively collaborating with solution providers for drug design and development.
What are the Key Advantages offered by Deep Learning in Drug Discovery and Diagnostics?
The use of deep learning in drug discovery has the potential to reduce capital requirements and the failure-to-success ratio, as algorithms are better equipped to analyze large datasets. Similarly, in diagnostics domain, deep learning technology can be used to assist medical professionals in medical imaging and interpretation. This enables quick and efficient diagnosis of disease indications at an early stage.
What are the Key Drivers of Deep Learning in Drug Discovery and Diagnostics Market?
In the last decade, the healthcare industry has witnessed an inclination towards the adoption of information services and digital analytical solutions.
This can be attributed to the fact that companies have recently shifted towards high-resolution medical images and electronic health and medical records, generating large and complex data, referred to as big data. In order to analyze such large, structured and unstructured datasets, efficient tools and technology, such as deep learning, are required. Thus, these massive datasets are anticipated to be a primary driver of technological advancements in the deep learning and artificial intelligence domain.
What are the Key Trends in the Deep Learning in Drug Discovery and Diagnostics Market?
Many stakeholders have been making consolidated efforts to forge alliances with other industry / non-industry players for research, software licensing and collaborative drug / solution development purposes. It is worth highlighting that over 240 clinical studies are being conducted to evaluate the potential of deep learning in diagnostics, highlighting the continuous pace of innovation in this field.
Moreover, the field is evolving continuously, as a number of start-ups have emerged with the aim of developing deep learning technologies / software. In this context, in the past seven years, over 60 companies providing deep learning-based solutions have been established. Given the inclination towards advanced deep learning technologies and their vast applications in the healthcare segment, we believe that the deep learning market is likely to evolve at a rapid pace over the coming years.
Frequently Asked Questions
Question 1: What is deep learning? What are the major factors driving the deep learning market focused on drug discovery and diagnostics?
Answer: The paradigm shift of industry players towards digitization and challenges associated with the drug discovery process have contributed to the overall adoption of deep learning technologies for drug discovery, leading to a reduced economic load. The potential of deep learning technologies in assisting medical personnel in an early-stage diagnosis of various disorders has fueled the adoption of such technologies in the diagnostics segment.
Question 2: Which companies offer deep learning technologies / services for drug discovery and diagnostics?
Answer: Presently, more than 200 players are engaged in the deep learning domain, offering technologies / services, specifically for drug discovery and diagnostics purposes.
Question 3: How much funding has taken place in field of deep learning in drug discovery and diagnostics?
Answer: Since 2019, more than USD 15 billion has been invested in the deep learning in drug discovery and diagnostics domain across multiple funding instances. Of these, the most prominent funding types included venture capital and grants, demonstrating high start-up activity in this domain.
Question 4: How many clinical trials, based on deep learning technologies, are being conducted?
Answer: Currently, more than 420 clinical trials are being conducted tor evaluate the potential of deep learning for diagnostic purposes. Of these, 63% of the trials are active.
Question 5: What is the likely cost saving potential associated with the use of deep learning-based technologies in diagnostics?
Answer: Considering the vast potential of artificial intelligence, deep learning technologies are believed to save around 45% of the overall drug diagnostic costs.
Question 6: Which therapeutic area accounts for the largest share in the deep learning for drug discovery market?
Answer: Presently, oncological disorders capture the largest share (close to 40%) of the deep learning in drug discovery market. However, therapeutic areas, such as cardiovascular and respiratory disorders are likely to witness higher annual growth rates in the upcoming years. This can be attributed to the increasing applications of deep learning technologies across drug discovery.
Question 7: Which region is expected to witness the highest growth rate in the deep learning market for diagnostics?
Answer: The deep learning market for diagnostics in North America is likely to grow at the highest CAGR, during the period 2023- 2035.
Key Topics Covered:
1. PREFACE
2. EXECUTIVE SUMMARY
3. INTRODUCTION
4. MARKET OVERVIEW: DEEP LEARNING IN DRUG DISCOVERY
4.1. Chapter Overview
4.2. Deep Learning in Drug Discovery: Overall Market Landscape of Service / Technology Providers
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters
4.2.4. Analysis by Application Area
4.2.5. Analysis by Focus Area
4.2.6. Analysis by Therapeutic Area
4.2.7. Analysis by Operational Model
4.2.7.1. Analysis by Service Centric Model
4.2.7.2. Analysis by Product Centric Model
5. MARKET OVERVIEW: DEEP LEARNING IN DIAGNOSTICS
5.1. Chapter Overview
5.2. Deep Learning in Diagnostics: Overall Market Landscape of Service / Technology Providers
5.2.1. Analysis by Year of Establishment
5.2.2. Analysis by Company Size
5.2.3. Analysis by Location of Headquarters
5.2.4. Analysis by Application Area
5.2.5. Analysis by Focus Area
5.2.6. Analysis by Therapeutic Area
5.2.7. Analysis by Type of Offering / Solution
5.2.8. Analysis by Compatible Device
6. COMPANY PROFILES
6.1. Chapter Overview
6.2. Aegicare
6.2.1. Company Overview
6.2.2. Service Portfolio
6.2.3. Recent Developments and Future Outlook
6.3. Aiforia Technologies
6.4. Ardigen
6.5. Berg
6.6. Google
6.7. Huawei
6.8. Merative
6.9. Nference
6.10. Nvidia
6.11. Owkin
6.12. Phenomic AI
6.13. Pixel AI
7. PORTER'S FIVE FORCES ANALYSIS
8. CLINICAL TRIAL ANALYSIS
9. FUNDING AND INVESTMENT ANALYSIS
10. START-UP HEALTH INDEXING
11. COMPANY VALUATION ANALYSIS
11.1. Chapter Overview
11.2. Company Valuation Analysis: Key Parameters
11.3. Methodology
11.4. Company Valuation Analysis: Publisher Proprietary Scores
12. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DRUG DISCOVERY
13. MARKET SIZING AND OPPORTUNITY ANALYSIS: DEEP LEARNING IN DIAGNOSTICS
14. DEEP LEARNING IN HEALTHCARE: EXPERT INSIGHTS
15. CONCLUDING REMARKS
16. INTERVIEW TRANSCRIPTS
17. APPENDIX 1: TABULATED DATA
18. APPENDIX 2: LIST OF COMPANIES AND ORGANIZATIONS
For more information about this report visit https://www.researchandmarkets.com/r/wv94in
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