Deep Learning in Diagnostics Market Report 2025

The deep learning in diagnostics market size has grown exponentially in recent years. It will grow from $2.56 billion in 2024 to $3.49 billion in 2025 at a compound annual growth rate (CAGR) of 36.1%. Growth in the historic period was driven by increasing availability of healthcare data from electronic health records, rising adoption of cloud-based diagnostic platforms, higher demand for faster diagnostic turnaround times, growing need for cost-effective diagnostic solutions in developing regions, and expanded use of deep learning in genomics and precision diagnostics.

The deep learning in diagnostics market size is expected to see exponential growth in the next few years. It will grow to $11.85 billion in 2029 at a compound annual growth rate (CAGR) of 35.8%. Growth in the forecast period is expected to result from rising demand for early and accurate disease detection, growing prevalence of chronic and lifestyle-related diseases, increased investment in AI healthcare research and development, stronger need to reduce diagnostic errors and improve accuracy, and greater use of digital pathology and radiology solutions. Primary trends anticipated for the forecast period include advancements in AI-powered 3D imaging, integration of AI with electronic health records, progress in predictive analytics for disease detection, incorporation of AI with genomics and multi-omics data, and innovations in automated image interpretation.

The growing trend of healthcare digitization is expected to drive the expansion of the deep learning in diagnostics market. Healthcare digitization refers to the integration of digital technologies in healthcare systems to improve efficiency, data management, accessibility, and patient care. This shift is fueled by the need to manage and securely exchange the increasing volumes of patient data, enabling better coordination and more informed decision-making. The digitization of healthcare generates vast amounts of data from sources such as medical imaging, electronic health records, and connected devices, creating a demand for deep learning in diagnostics. Deep learning technologies can process this data efficiently and deliver faster, more accurate insights than traditional methods. For example, the Department of Health and Social Care in the UK reported in June 2022 that by March 2025, all NHS trusts are expected to have implemented electronic health records, up from 90% adoption by December 2023. This rise in healthcare digitization is driving the demand for deep learning in diagnostics.

Companies in the deep learning diagnostics market are advancing AI-powered solutions to improve diagnostic accuracy, speed, and personalized care. An AI-driven deep learning solution leverages artificial intelligence and layered neural networks to automatically analyze complex medical data, identify patterns, and produce highly accurate diagnostic insights with minimal human involvement. For instance, in May 2025, GE Healthcare Technologies, a U.S.-based medical technology company, launched CleaRecon DL, designed to enhance image reconstruction and diagnostic accuracy. This system improves cone-beam CT (CBCT) images by removing streak artifacts, providing clearer and more accurate images for interventional procedures. Clinical studies have shown that the technology results in 98% clearer images and 94% improved confidence among clinicians. This solution ultimately improves patient outcomes by streamlining workflow and enabling more effective, image-guided treatments.

In November 2022, Anumana Inc., a U.S.-based AI health technology company, acquired NeuTrace Inc. for an undisclosed sum. This acquisition allows Anumana to strengthen its position in AI medical software for cardiac electrophysiology by integrating NeuTrace’s EP Data Biome platform and AI-enabled electrophysiology applications into its offerings. NeuTrace specializes in AI-powered diagnostic solutions, with deep learning being a core technology for analyzing medical data such as images and signals. This partnership is expected to enhance real-time data integration and clinical decision support in the cardiac electrophysiology field.

Major players in the deep learning in diagnostics market are International Business Machines Corporation, Siemens Healthineers AG, Koninklijke Philips N.V., GE HealthCare Technologies Inc., Tempus AI Inc., Qure.ai Technologies Pvt. Ltd., Freenome Holdings Inc., PathAI Inc., Aidoc Medical Ltd., Viz.ai Inc., SOPHiA GENETICS SA, Lunit Inc., Paige.AI Inc., Beijing Infervision Technology Co. Ltd., Indica Labs Inc., CureMetrix Inc., Deep Bio Inc., Enlitic Inc., ScreenPoint Medical B.V., VUNO Inc., Mindpeak GmbH, and Arterys Inc.

North America was the largest region in the deep learning in diagnostics market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in deep learning in diagnostics report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the deep learning in diagnostics market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The sudden escalation of U.S. tariffs and the consequent trade frictions in spring 2025 are severely impacting the healthcare sector, particularly in the supply of critical medical devices, diagnostic equipment, and pharmaceuticals. Hospitals and healthcare providers are facing higher costs for imported surgical instruments, imaging equipment, and consumables such as syringes and catheters, many of which have limited domestic alternatives. These increased costs are straining healthcare budgets, leading some providers to delay equipment upgrades or pass on expenses to patients. Additionally, tariffs on raw materials and components are disrupting the production of essential drugs and devices, causing supply chain bottlenecks. In response, the industry is diversifying sourcing strategies, boosting local manufacturing where possible, and advocating for tariff exemptions on life-saving medical products.

Deep learning in diagnostics refers to the application of advanced artificial intelligence techniques, particularly multi-layered neural networks, to analyze complex medical data such as images, signals, or patient records. It assists in identifying patterns, detecting diseases, predicting outcomes, and supporting clinical decision-making with higher speed and accuracy compared to traditional methods.

The primary components of deep learning in diagnostics include software, hardware, and services. Software consists of programs, instructions, or data that direct a computer or device to perform specific tasks. Deployment methods include cloud-based and on-premises solutions. Applications span medical imaging, pathology, genomics, drug discovery, and other areas, serving end users such as hospitals, diagnostic laboratories, research institutes, and others.

The deep learning in diagnostic market research report is one of a series of new reports that provides deep learning in diagnostic market statistics, including deep learning in the diagnostic industry's global market size, regional shares, competitors with deep learning in diagnostic market share, detailed deep learning in diagnostic market segments, market trends and opportunities, and any further data you may need to thrive in the deep learning in the diagnostic industry. This deep learning in diagnostic market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The deep learning in diagnostics market includes revenues earned by entities by providing services such as customized algorithm development services, real-time diagnostic monitoring services, genomic and biomarker analysis services, patient data integration and interpretation services, and disease risk prediction services. The market value includes the value of related goods sold by the service provider or included within the service offering. The deep learning in diagnostics market also consists of sales of graphics processing units, edge AI devices, AI-enhanced CT scanners, and ultrasound devices. Values in this market are ‘factory gate’ values; that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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