Deep Learning Market

Deep Learning Market Overview

The deep learning market (for healthcare and drug discovery) is estimated to be $34.5 billion in 2023 and is expected to grow at a CAGR of 21.9% during the forecast period.

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 led to the emergence 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 that imparts 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, making it a promising approach for big data analysis. Owing to the distinct characteristic of deep learning algorithm to imitate the human brain, it is currently being deployed in the life sciences industry, primarily for the purposes of drug discovery and diagnostics. Within the healthcare market, the primary application of deep learning is in diagnostics. Considering the challenges associated with drug discovery and drug development, such as the high attrition rate and increased financial burden, deep learning has been found to improve the overall drug discovery productivity. Recent advancements in the deep learning market have demonstrated its potential in other healthcare-associated segments, such as medical imaging, molecular profiling, virtual screening and data analysis. Driven by the ongoing pace of innovation and the profound impact of this field of computational medicine, deep learning market in healthcare and drug discovery is anticipated to witness substantial market growth during the forecast period.

Key Market Insights

The Deep Learning in Drug Discovery Market and Deep Learning in Diagnostics Market (2nd Edition), 2023-2035: Distribution by Therapeutic Area (Oncological Disorders, Infectious Diseases, Neurological Disorders, Immunological Disorders, Endocrine Disorders, Cardiovascular Disorders, Respiratory Disorders, Eye Disorders, Musculoskeletal Disorders, Inflammatory Disorders and Other Disorders) and Key Geographical Regions (North America, Europe, Asia Pacific and Rest of the World): Industry Trends and Global Forecasts, 2023-2035 market report features an extensive study of the current market landscape, market size, and future opportunities for the deep learning market within the healthcare industry. Further, the market report highlights the efforts of several stakeholders engaged in this rapidly emerging segment of the pharmaceutical industry. Key takeaways of the deep learning market report are briefly discussed below.

Need for 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 market, 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.

Key Market Drivers: Analysis of Big Data with the Help of Deep Learning

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 datasets, efficient tools and technology, such as deep learning, are required. Thus, the emergence of big data is anticipated to be a primary driver of the adoption of deep learning and artificial intelligence in the healthcare industry.

Deep Learning Market Analysis: The Market is Dominated by Diagnostics Segment 

Currently, more than 200 companies are focused on providing deep learning services and deep learning technologies for drug discovery and diagnostic purposes. The primary focus areas of these companies include big data analysis, medical imaging, medical diagnosis and molecular data analysis. The deep learning in diagnostics segment features the presence of 139 companies, which is dominated by the presence of small companies (49%) owing to the technological advances in this field. Further, these companies are engaged in offering services across a wide range of therapeutic areas, with the primary focus on oncological disorders. 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. In this context, image processing services emerged as the most prominent type of service offered by the companies (83%) engaged in the deep learning in diagnostics domain.

Deep Learning Market Size for Drug Discovery: North America and Europe are the Leading Regions

Deep learning market for drug discovery is estimated to be $0.59 billion in 2023. In terms of therapeutic area, oncology is expected to capture the largest market share. This is due to the fact that deep learning allows the optimized categorization of histopathological images by comparing it with a vast dataset of such images, that enables enhanced diagnosis, prognosis and targeted treatment selection for oncological disorders. Lately, the industry has also witnessed the development of advanced deep learning technologies and software. These technologies possess the ability to obviate the concerns associated with the conventional drug discovery process and 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 23.1% between 2023 and 2035. In terms of geography, the market is anticipated to grow at a faster pace in North America and Europe by 2035.

Deep Learning Market Size for Diagnostics Application: Musculoskeletal and Eye Disorders are the Fastest Growing Market Segments by Therapeutic Area

Deep learning market for diagnostics in musculoskeletal and eye disorders is estimated to be $3.9 billion in 2035. The adoption of deep learning technologies to assist medical diagnosis, primarily through medical imaging, has increased in the recent past. The global deep learning market focusing on diagnostics is anticipated to grow at a CAGR of 19.8% 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 musculoskeletal and eye disorders is anticipated to grow at a relatively faster pace by 2035, growing at a CAGR of 23% and 21%, respectively.

Market Segmentation: Deep Learning in Diagnostics Holds the Largest Market Share

As of 2023, the deep learning market size for diagnostics applications is estimated to be $3.1 billion. This is because of increased 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 CAGR of 23.1% during the forecast period with several pharmaceutical companies actively collaborating with solution providers for drug design and development.

Deep Learning Market Trends: Over 240 Clinical Trials are Being Conducted to Evaluate Application of Deep Learning in Diagnostics

Several industry and academic players are actively conducting clinical studies for the evaluation of applying deep learning algorithms for diagnostic purposes. Over 240 clinical studies are completed / 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 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, the deep learning market is likely to evolve at a rapid pace over the forecast period.

Top Deep Learning Companies (Drug Discovery)

Examples of top deep learning companies for drug discovery (which have also been captured in this market research report) include Atomwise, Benevolent.ai, Cloud Pharmaceuticals, Deargen, Deep Cure, Exscientia, GNS Healthcare, Insilico Medicine, Isomorphic Labs, Juvena Therapeutics, Merative, Optibrium,x and Valence Discovery. This market report includes and easily searchable excel database for all the deep learning companies offering technologies for drug discovery.

Top Deep Learning Companies (Diagnostics)

Examples of top deep learning companies for diagnostics (which have also been captured in this report) include Avalon AI, Behold.ai, Blueberry Diagnostics, Deep Longevity, Esaote, Enlitic, Flatiron Health, H2O.ai, Huawei, InMed Prognostics, Kheiron Medical, Mediwhale, Nference, and Visiopharm. This market report includes and easily searchable excel database for all the companies offering deep learning for diagnostics.

Recent Developments in Deep Learning Market for Drug Discovery

Several recent developments have taken place in the field of deep learning in drug discovery. We have outlined some of these recent initiatives below. These developments, even if they took place post the release of our market report, substantiate the overall market trends that have been outlined in our analysis.

• In July 2023, Aiforia entered into a collaboration with Orion for the development of AI-based image analysis solutions for preclinical research and product development. 
• In July 2023, NVIDIA announced the investment of USD 50 million in Recursion Pharmaceuticals with the aim to create artificial intelligence assisted drug discovery models.  
• In May 2023, Google launched AI-powered tools, namely Multiomics Suite and Target and Lead Identification Suite, to accelerate drug discovery in the field of precision medicine.

Scope of the Report

The study presents an in-depth analysis of the various firms / organizations that are engaged in the deep learning market for drug discovery and diagnostics, across different segments as defined in the below table:

Report Attributes	Details
Base Year	2023
Forecast Period	2023-2035
Market Size (Drug Discovery, 2023)	$0.59 Billion
Market Size (Diagnostics, 2023)	$3.1 Billion
CAGR (Drug Discovery)	23.1%
CAGR (Diagnostics)	19.8%
Therapeutic Areas	Oncological Disorders  Infectious Diseases  Neurological Disorders  Immunological Disorders  Endocrine Disorders Cardiovascular Disorders  Respiratory Disorders  Eye Disorders  Musculoskeletal Disorders  Inflammatory Disorders Other Disorders
Key Geographical Regions	North America  Europe  Asia Pacific  Rest of the World
Key Companies Profiled	Aegicare  Aiforia Technologies  Ardigen  Berg  Google  Huawei  Merative  Nference  Nvidia  Owkin  Phenomic AI  Pixel AI
Customization Scope	15% Free Customization Option (equivalent to 5 analyst’s working days)
PowerPoint Presentation (Complimentary)	Available
Excel Data Packs (Complimentary)	Market Landscape Analysis (Drug Discovery)  Market Landscape Analysis (Diagnostics)  Clinical Trial Analysis  Funding Analysis  Start-up Health Indexing  Company Valuation Analysis  Market Sizing and Opportunity Analysis (Drug Discovery) Market Sizing and Opportunity Analysis (Diagnostics)

The market report presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in the deep learning market, across different geographies. Amongst other elements, the report includes:

• An executive summary of the insights captured during our research, offering a high-level view on the current state of deep learning market for drug discovery and diagnostics and its likely evolution in the mid-to-long term.
• A general introduction to the big data revolution in the medical industry. The chapter presents information on artificial intelligence, machine learning and deep learning algorithms. Further, it concludes with a discussion on various applications of deep learning within the healthcare industry.
• A detailed assessment of the market landscape of more than 70 deep learning companies offering technologies and services for the purpose of drug discovery, based on several relevant parameters, such as year of establishment, company size, location of headquarters, application area (drug discovery, and drug discovery and diagnostics), focus area (big data analysis, genomic data analysis, molecular data analysis, medical diagnosis, medical imaging and EMR analysis), therapeutic area (oncological disorders, neurological disorders, infectious diseases, immunological disorders, cardiovascular disorders, inflammatory disorders, metabolic disorders, pulmonary disorders, hepatic disorders, musculoskeletal disorders, dermatological disorders, gastrointestinal disorders and other disorders), operational model (service provider, technology / software developer and in-house developer), along with information on the company’s service and product centric models. 
• A detailed assessment of the overall market landscape of more than 130 deep learning companies offering technologies / services for diagnostics, based on several relevant parameters, such as year of establishment, company size, location of headquarters, application area (diagnostics, and drug discovery and diagnostics), focus area (big data analysis, genomic data analysis, medical screening, medical diagnosis, medical imaging, surgery planning and EMR analysis), therapeutic area (oncological disorders, neurological disorders, cardiovascular disorders, pulmonary disorders, infectious diseases, musculoskeletal disorders, metabolic disorders, ophthalmic disorders, hepatic disorders, gastrointestinal disorders, gynecological disorders, hematological disorders, urological diseases, dermatological disorders and other disorders), type of offering / solution (analysis reports, image processing, cloud based solutions and biomarker identification), along with information on various compatible device (CT, MRI, Ultrasound, X-Ray, Mammography, PET and others).
• Elaborate profiles of key players developing technologies and offering services related to deep learning, specifically for drug discovery and diagnostics, located across North America, Europe and Asia Pacific (shortlisted based on a proprietary criterion), including a brief overview of the company, along with details related to its financial information (if available), service portfolio, recent developments and an informed future outlook.
• A qualitative analysis, highlighting the five competitive forces prevalent in deep learning industry, including threats for new entrants, bargaining power of companies using deep learning-based drug discovery and diagnostics, bargaining power of drug developers, threats of substitute technologies and rivalry among existing competitors.  
• An analysis of completed and ongoing clinical trials which involve deep learning in diagnostics, based on several relevant parameters, such as trial registration year, trial status, patient enrollment, type of sponsor / collaborator, therapeutic area, trial focus area, study design, and geography. In addition, the chapter highlights the most active industry and non-industry players (in terms of number of clinical trials conducted).
• A detailed analysis of various investments made in deep learning industry, during the period 2019-2022, based on several relevant parameters, such as year of funding, amount invested, type of funding (seed financing, venture capital financing, IPOs, secondary offerings, debt financing, grants and other offerings), focus area, therapeutic area, and geography. In addition, the chapter highlights the most active players (in terms of number of funding instances and amount invested) and key investors (in terms of number of funding instances).
• An analysis of the start-ups / small players (established post 2015, with less than 50 employees) engaged in the deep learning market focused on drug discovery and diagnostics, based on several relevant parameters, such as focus area, therapeutic area, operational model, compatible device, type of offering and start-up health indexing.
• An elaborate valuation analysis of companies that are involved in the deep learning in drug discovery and diagnostics market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.

One of the key objectives of this market report was to estimate the current market size, opportunity, and future growth potential of deep learning market for drug discovery and diagnostic purposes over the coming years. We have provided informed estimates on the likely evolution of the market in the mid-to-long term, for the forecast period, 2023-2035. Our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as therapeutic area (oncological disorders, infectious diseases, neurological disorders, immunological disorders, endocrine disorders, cardiovascular disorders, respiratory disorders, ophthalmic disorders, musculoskeletal disorders and other disorders) and key geographical regions (North America, Europe, Asia Pacific and Rest of the World). Further, the chapter includes estimates of the likely cost saving potential of deploying deep learning technologies in the healthcare sector. In order to account for future uncertainties associated with some of the key parameters and to add robustness to our model, we have provided three market forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry’s evolution.

The opinions and insights presented in the report were influenced by discussions held with stakeholders in this industry. The report features detailed transcripts of interviews held with the following individuals:

• Chief Executive Officer, Small Company, India
• Founder and Chief Executive Officer, Small Company, India
• Former Vice President of Product and Software Development, Mid-sized Company, USA
• Head of Strategy and Marketing, Mid-sized Company, USA
• Chief Technical Officer, Small Company, India and Chief Operating Officer, Small Company, India
• Former Research Scientist, Small Company, Sweden
• Chief Executive Officer, Small Company, South Korea
• Chief Executive Officer, Mid-sized company, USA and Commercial Strategy and Operations Lead, Mid-sized company, USA

All actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this report are in USD, unless otherwise specified.

Frequently Asked Questions

Question 1: What is deep learning?

Answer: Deep learning is a machine learning technique that allows computers to process and analyze data simulating the human brain, in order to model and solve complex patterns and problems to produce accurate insights.

Question 2: What are the deep learning market drivers for 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 3: How many companies offer deep learning technologies / services for drug discovery and diagnostics?

Answer: Presently, more than 200 deep learning companies are engaged in the deep learning market, offering technologies / services, specifically for drug discovery and diagnostics purposes.

Question 4: How much money has been invested in the deep learning industry for drug discovery and diagnostics?

Answer: Since 2019, more than $15 billion has been invested in the deep learning market in drug discovery and diagnostics across multiple funding instances.

Question 5: How much cost do deep learning technologies save 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: How many clinical trials, based on deep learning technologies, are being conducted?

Answer: Currently, more than 420 clinical trials are being conducted to evaluate the potential of deep learning for diagnostic purposes.

Question 7: How big is deep learning market for drug discovery and diagnostics market?

Answer: The deep learning market size (for drug discovery and diagnostics) is estimated to be $34.5 billion in 2023.

Question 8: Which region has the highest growth rate in the deep learning market?

Answer: The deep learning market for diagnostics in North America is likely to grow at the highest CAGR, during the period 2023- 2035.

Question 9: What is the growth rate (CAGR) of the deep learning in drug discovery and diagnostics market?

Answer: Deep learning market is anticipated to grow at a CAGR of 21.9% between 2023 and 2035.