Traditional techniques for medical data interpretation are often time-consuming and expensive. Deep learning offers a promising solution in drug discovery and diagnosis.
Roots Analysis has published a new study, “Deep Learning in Drug Discovery Market and Deep Learning in Diagnostics Market (2nd Edition), 2023-2035.” The report analyzes the current market landscape and growth potential for deep learning-based technologies and solutions in healthcare. As traditional statistical tools and techniques for medical data interpretation are often time-consuming and expensive, deep learning could be a promising solution in drug discovery and diagnosis.
The current drug discovery market landscape: Foundation for growth
Over 70 players offer deep learning-powered services and technologies for drug discovery with small players dominating the current market landscape. More than 130 players offer deep learning technologies for diagnostic applications. In the last 8 years, over 35 start-ups focused on deep learning in diagnostics emerged, while clinical activity for deep learning technologies increased by 40% during the period 2018-2022.
More than USD 15 billion has been invested in deep learning technologies by both private and public investors across 200+ instances. Between 2019 and 2022, the majority of the amount was raised through venture capital rounds (62%) and secondary offerings (16%). The use of deep learning-based solutions in drug discovery could enable up to 20% cost savings for researchers and healthcare companies.
The North America and Asia Pacific markets are expected to capture an overwhelming majority of the deep learning in this market by 2035, while the European market is likely to grow at a relatively faster pace than current expansion. In 2035, deep learning in the drug discovery market for oncological disorders is expected to make up the majority share of the total market. The report predicts the deep learning in diagnostics market will grow at a CAGR of 20% by 2035, with majority of the revenues (34%) coming from deep learning technologies being used for the diagnosis of oncological disorders.
The report features an section estimating the cost-saving potential associated with deep learning-based solutions in drug discovery and diagnostics, predicting the adoption of such technologies can save more than USD 22.3 billion in diagnostics by 2035.