Roots Analysis has recently published “Deep Learning in Drug Discovery Market and Deep Learning in Diagnostics Market (2nd Edition), 2023-2035” report. It features an extensive study of the current market landscape and the likely future potential of the deep learning solutions market within the healthcare domain.
Key Inclusions
§ An executive summary of the insights captured during our research. It offers 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 overview of big data revolution in the medical industry. It also 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 companies offering deep learning technologies / 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 market landscape of more than 130 companies offering deep learning 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). Each profile includes a brief overview of the company, along with details related to its financial information (wherever available), service portfolio, recent developments and an informed future outlook.
§ A qualitative analysis, highlighting the five competitive forces prevalent in this domain, 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, 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 by players engaged in this domain, 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.
The report also features the likely distribution of the current and forecasted opportunity across important market segments, mentioned below:
§ Therapeutic Area
§ Oncological Disorders
§ Infectious Diseases
§ Neurological Disorders
§ Immunological Disorders
§ Endocrine Disorders
§ Cardiovascular Disorders
§ Respiratory Disorders
§ Ophthalmic Disorders
§ Musculoskeletal Disorders
§ Inflammatory Disorders
§ Other Disorders
§ Key Geographical Regions
§ North America
§ Europe
§ Asia Pacific
§ Rest of the World
The opinions and insights presented in the report were also influenced by discussions held with eminent stakeholders in the industry. The report features detailed transcripts of interviews held with the following industry stakeholders:
§ Avi Veidman (Chief Executive Officer, Nucleai), Yoav Blum (Director of AI, Nucleai) and Ken Bloom (Head of Pathology, Nucleai)
§ Kevin Choi (Chief Executive Officer, Mediwhale)
§ Babak Rasolzadeh (Former Vice President, Product and Software Development, Arterys)
§ Vikas Karade (Founder, Chief Executive Officer, AlgoSurg)
§ Walter de Back (Former Research Scientist, Context Vision)
§ Mausumi Acharya (Chief Executive Officer, Advenio Technosys)
§ Carla Leibowitz (Former Head, Strategy and Marketing, Arterys)
§ Deekshith Marla (Founder, Chief Technology Officer, Arya.ai) and Sanjay Bhadra (Chief Business Officer, Arya.ai)
Frequently Asked Questions
§ What is deep learning? What are the major factors driving the deep learning market focused on drug discovery and diagnostics?
§ Which companies offer deep learning technologies / services for drug discovery and diagnostics?
§ How much funding has taken place in field of deep learning in drug discovery and diagnostics?
§ How many clinical trials, based on deep learning technologies, are being conducted?
§ What is the likely cost saving potential associated with the use of deep learning-based technologies in drug discovery?
§ Which therapeutic area accounts for the largest share in the deep learning for drug discovery market?
§ Which region is expected to witness the highest growth rate in the deep learning market for diagnostics?
To view more details on this report, click on the link
https://www.rootsanalysis.com/reports/view_document/deep-learning-in-drug-discovery-market/156.html
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