The Artificial Intelligence/Machine Learning Medical Device Market Size is valued at 6.10 Billion in 2024 and is predicted to reach 52.09 Billion by the year 2031 at a 24.1% CAGR during the forecast period for 2024-2031.
The market for artificial intelligence/machine learning-enabled medical devices is very fragmented, and each year, more products bearing the FDA and CE marks are approved. Due to continuous technical advancements and investments in healthcare AI, this industry still has a substantial amount of growth potential. The advancement of AI-based medical devices that enable diagnostic accuracy and productivity present a potential opportunity for the worldwide artificial intelligence/machine learning medical device market.
The market is anticipated to develop quickly following the COVID-19 pandemic, mostly because of the increasing use of AI-based solutions brought on by the epidemic. The market will grow because of an increase in research into AI-enabled products, improvements in deep learning and machine learning algorithms, the release of new products onto the market, the emergence of regional businesses, and the expanding use of AI-based products for therapeutic purposes.
The Artificial Intelligence/Machine Learning Medical Device Market is segmented based on product type, clinical area. The product type segment includes System/Hardware and Software-as-a-Medical Device. By Clinical Area application, the market is divided into Radiology, Cardiology, and Hematology. The radiology segment it is segmented (by Type) includes Diagnostic Assistance, Imaging, and Image Reconstruction. The cardiology segment (by Type) includes Electrocardiography-Based Arrhythmia Detection and Hemodynamics and Vital Signs Monitoring.
The market for AI/ML medical devices will lead in the clinical area segment for radiology. AI-enabled medical devices are being developed for various clinical applications, including radiology, cardiology, hematology, obstetrics, gastrointestinal, and pathology. AI in radiology can support radiologists in data interpretation and diagnostic confirmation. For a variety of tasks in radiology, including the identification of suspicious lesions, improving the quality of imaging, picture segmentation and contouring, and image reconstruction, artificial intelligence (AI) is being used.
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America are the five geographic areas into which the worldwide market is divided. Significant R&D investors like Oracle Corporation, IBM Corporation, and Amazon.com increase the market size in the area. Furthermore, it is anticipated that substantial expenditures and the availability of existing IT infrastructure would fuel market expansion in North America.
Customers now have easier access to services and products that use AI, which impacts the regional economy. The European Union proposed a 10.4 billion USD budget for the Digital Europe Programme in June 2018 for the years 2021–2027. Over the course of the prediction, Asia Pacific is anticipated to advance at a speedier rate. A lively and robust startup ecosystem is present in the area's rising economies, including China, India, and the Philippines. An expanding trained labor force that fuels regional market expansion aids this ecosystem.
| Report Attribute | Specifications |
| Market size value in 2024 | USD 6.10 Bn |
| Revenue forecast in 2034 | USD 52.09 Bn |
| Growth rate CAGR | CAGR of 24.1% from 2025 to 2034 |
| Quantitative units | Representation of revenue in US$ Billion, and CAGR from 2024 to 2031 |
| Historic Year | 2021 to 2024 |
| Forecast Year | 2025-2034 |
| Report coverage | The forecast of revenue, the position of the company, the competitive market statistics, growth prospects, and trends |
| Segments covered | Product Type, Clinical Area |
| Regional scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; Japan; South Korea; Southeast Asia |
| Competitive Landscape | Aidoc Medical, Ltd., Canon Inc., CellaVision AB, Clarius Mobile Health Corp., General Electric Company, Hyperfine Inc., Koninklijke Philips N.V., Medtronic plc, Nanox.AI Ltd., Paige.AI |
| Customization scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing and available payment methods | Explore pricing alternatives that are customized to your particular study requirements. |
Artificial Intelligence/Machine Learning Medical Device Market By Product Type-
Artificial Intelligence/Machine Learning Medical Device Market By Clinical Area-
By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.