US AI-Enabled Medical Devices Market 2025 – 2034

As per the US AI-Enabled Medical Devices Market analysis conducted by the CMI team, the US AI-enabled medical devices market is expected to record a CAGR of 39.37% from 2025 to 2034. In 2025, the market size was USD 19.89 Billion. By 2034, the valuation is anticipated to reach USD 392.24 Billion.

Overview

The growth of the AI-enabled medical devices market in the US does indicate a shift in the direction of more widespread use of AI in both institutional and consumer-facing healthcare applications. The increased demand for next-generation medical devices providing actionable insights, personalized treatment options, and real-time data is expected to keep the cash registers ringing for the US AI-enabled medical devices market.

Market expansion is getting supported by growing adoption of AI in the healthcare settings and corresponding demand for improved diagnostic precision and treatment accuracy. Also, a growing focus on value-based healthcare and cost-cutting is propelling AI-powered medical devices that are capable of improving efficiency and reducing human glitches. Preference for healthcare providers who provide integrated solutions that are capable of combining diagnostic capabilities and treatment recommendations is creating opportunities for newfangled AI-enabled platforms.

Key Trends & Drivers

• Rise in Preference for Improved Diagnostic Precision

AI does offer a raised accuracy quotient, which helps in reducing misdiagnoses. This, in turn, improved patient outcomes. AI also analyses huge patient data for tailoring treatment plans on the basis of individual genetic make-up, medical history, and lifestyle. Diagnosis of chronic diseases at an early stage does improve survival rates on a significant note and also lowers the cost of treatments, which is one of the key focus areas for the AI-enabled devices.

AI is also capable of automating routine tasks, which does ease the burden on the existing healthcare professionals. Innovations such as computer vision, machine learning, natural language processing (NLP), and 5G connectivity are expediting adoption of AI in medical devices. Visible funding from the established players as well as start-ups does fuel innovation with worldwide adoption of ML/AI in healthcare.

What’s trending in the US AI-enabled Medical Devices Market?

Evolution of AI algorithms that include natural language processing, deep learning, and computer vision is propelling AI-enabled medical device capacities. Improvements in algorithm precision do reduce error rates and improve diagnostic accuracy. Such technological enhancements enable expanded applications encompassing predictive risk modelling, AI-assisted imaging, and automated patient monitoring. On these grounds, Clairity, Inc., in June 2025, did become the very first AI platform authorized by the US FDA for prediction of breast cancer. In other words, it received De Novo authorization for CLAIRITY BREAST. This platform does foretell a woman’s five-year risk of getting breast cancer using merely routine screening mammograms.

What would be Business Impact of the US tariffs on the US AI-enabled Medical Devices Market?

The US medtech manufacturing is going strong and reduced tariffs would be fueling job growth and more manufacturing in the US This translates into the fact that there would be better access enabled for lifesaving technologies with lower costs to the American patients and hospitals. The Department of Commerce is also seeking inputs from the companies regarding their projected demand for such products and whether domestic production could meet the role played by foreign supply chains and local demand.

Key Threats

The AI-enabled medical devices market, in spite of its positive outlook, does face challenges with regard to regulatory approval, system integration complexities, and data privacy concerns. Navigation of complex regulatory landscapes, particularly in regions such as the US does present hurdles for the companies that are looking to bring AI-powered medical devices to the market.

The regulatory bodies are struggling to keep pace with speedy advancements in AI, thereby resulting in delays in the approval process. Moreover, data privacy does remain a matter of concern, especially with large amounts of sensitive health information that are being processed by the AI devices. Such challenges could slow down the adoption of AI-powered devices, which could, in turn, hinder market growth.

Opportunities

AI algorithms pertaining to pattern analysis, image recognition, and predictive diagnostics can be increasingly deployed in medical imaging systems. This could result in improvement in rates of detection, particularly in cardiovascular diagnostics and cancer screening. The AI-powered tools are already surpassing the conventional tools by offering more precise and quicker diagnoses, which is cutting down on the time of treatment.

Moreover, the trend toward AI-enabled patient management tools is bound to reshape the way clinics and hospitals track patient progress, go for treatment regimens, and estimate medical needs. On the whole, such trends do reflect a shift in the direction of real-time, more personalized, and data-driven healthcare practices poised to improve patient outcomes through streamlining of healthcare workflows.

Category Wise Insights

By Component

• Software

The software segment holds over 50% of the market share. This is due to the fact that clinics and hospitals heavily depend on AI-driven software for interpreting imaging scans, predicting patient outcomes, and optimizing the treatment strategies. The flexibility of the software lets integration happen with the existing medical devices, thereby expanding its adoption without visible infrastructure alterations.

Growing focus on Software as a Medical Device (SaMD) has further sped up this segment’s expansion. For example, TeraRecon’s novel Intuition product line was upgraded in October 2024. It was delivered using a Software as a Service (SaaS) platform. It offers cardiac magnetic resonance (MR) workflows.

• Hardware

Advancements in AI hardware inclusive of efficient embedded chips and high-performance processors allow for data processing at the local level, improving speed, lowering latency, and enhancing privacy in the AI-enabled devices such as edge computing modules and smartphones. Application-specific integrated circuits (ASICs), graphics processing units (GPUs), and neural processing units (NPUs) are those high-performance units necessary in order to run complex AI algorithms like language processing and image analysis directly on the devices.

• Services

The services segment is expanding, with healthcare providers increasingly relying on specialized support for deployment, integration, and maintenance of AI technologies. Also, the hospitals are asking for partnerships with the service providers for real-time performance tracking, predictive analysis, and integration of AI with on-premises and cloud infrastructure. Having these capabilities outsourced lets healthcare facilities concentrate on clinical delivery while technological development happens in the background.

By Technology

• Machine Learning

The machine learning segment accounts for more than 35% of the market share due to machine learning’s improved data interpretation capacities, increased precision & predictive power, ability to support continuous and remote monitoring, and widespread integration in clinical and consumer devices. ML algorithms are broadly used in ECG analysis, imaging devices, and remote monitoring devices, wherein they improve decision-making.

• Natural Language Processing (NLP)

NLP lets AI devices interpret and respond to the human language, thereby making interactions with voice assistants more efficient and intuitive. Advancements in NLP have noticeably improved the efficiency of the AI assistants, thereby resulting in rising adoption in business and personal contexts for tasks such as scheduling meetings and managing the inquiries. NLP does power virtual assistants and power chatbots, wherein businesses get provided with tools for handling customer inquiries.

• Computer Vision

Computer vision lets machines perform complex visual tasks like detection of defects during production, recognition of objects in the robotic systems, and automation of checkouts in retail, thereby reducing the manual labor. In the manufacturing sector, computer vision systems do inspect products for defects, monitor quality, and ascertain precise assembly. The retailers utilize AI-powered computer vision for personalized marketing, smart inventory management, and virtual try-on experiences.

• Context-Aware Computing

The context-aware computing segment is expected to witness the highest CAGR during the forecast period. This is owing to the ability of context-aware computing to improve clinical decision-making through integrating contextual information like patient history, patient data’s real-time analysis, and support extended for personalized treatment pathways, eventually enhancing operational efficiency, diagnostic accuracy, and patient outcomes.

By Therapeutic Area

• Cardiology

The advancements in AI, ML, and deep learning technologies are capable of enabling the development of refined algorithms for analyzing complex medical data like genetic information and imaging for fostering early diagnosis, better patient outcomes, and personalized treatment plans. The AI algorithms also excel in the detection of subtle abnormalities with respect to imaging studies such as MRIs and echocardiograms, usually with precision comparable to ace cardiologists.

• Neurology

The neurology segment is driven by the growing prevalence of various neurological disorders like Alzheimer’s disease, stroke, Parkinson’s disease, and epilepsy, along with the increased need for advanced monitoring and diagnostic solutions. Growing adoption of the AI-powered tools for neuromonitoring, brain imaging, and predictive analysis is fueling the growth of this segment further.

• Radiology

Radiology segment holds the largest market share due to the large-scale adoption of AI-driven imaging tools for image interpretation, early disease diagnosis, decision support, and workflow automation. Increase in the volume of diagnostic imaging procedures, the growing demand for enhanced efficiency and precision, and the integration of the AI algorithms in various modalities such as X-Ray, MRI, and CT have strengthened the position of radiology in the market.

• Oncology

Oncology does facilitate analysis of vast clinical and genomic datasets for uncovering the cancer-related mutations and predicting drug responses. The AI-powered algorithms are capable of analysing medical imaging data like CT scans, mammograms, and MRIs for identifying suspicious tumors or lesions at an earlier stage. Such tools aid oncologists and radiologists in detecting cancer more efficiently and precisely owing to timely intervention.

• Mental and Behavioral Health

AI helps in the quick suggestion of personalized treatment plans, extends support to early diagnosis of mental disorders such as anxiety, depression, and post-traumatic stress disorder (PTSD), and does provide tools like AI-powered chatbots, mobile mental health applications, and virtual therapists for engaging patients in real time.

• Ophthalmology

AI is fast transforming vision sciences by enhancing diagnostic precision, widening access to quality eye care, and streamlining clinical workflow. As per the CDC, the number of Americans battling glaucoma was 4.22 million in 2022 alone. AI algorithms are capable of rapidly analysing complex retinal images, thereby enabling early detection and treatment. The AI systems have already shown higher sensitivity and specificity in the identification of diabetic retinopathy, which lets timely interventions happen and reduce risk of losing vision.

By End-use

• Hospitals & Clinics

The hospitals & clinics segment holds over 30% of the market share owing to higher imaging volumes, strong infrastructure, and integration capabilities needed for supporting the AI-enabled clinical decision and diagnostic support tools. Plus, hospitals are equipped with electronic medical records (EMR) systems and PACS (Picture Archiving and Communication Systems), thereby allowing for smooth integration of AI solutions into clinical workflows. Hospitals also participate in various clinical trials and ink collaboration agreements with the academic institutions for testing and validating the novel AI technologies.

• Diagnostic Centers

Diagnostic centers are using AI for optimizing workflows, improving patient outcomes, and reducing the operational costs. AI-powered tools do smoothen tasks like prior authorization, medical coding, appointment scheduling, billing, and documentation, which saves on time and reduces errors. Growing adoption of EHRs and a push in the direction of value-based care models do accelerate demand for automation further, which is being incorporated by the diagnostic centers.

• Ambulatory Surgical Centers (ASCs)

AI algorithms, in ASCs, improve operational efficiency, enhance patient outcomes, and cut down on administrative burden through real-time clinical decision support, predictive analytics for inventory and scheduling, automated data capture for claims processing and documentation, and AI-assisted patient education and communication. AI does analyse historical data for forecasting surgical volume, optimizing operating room schedules, and maximizing usage of block time, thereby resulting in an increase in output.

• Home Care Settings

The home care settings segment is supported by the rising demand for personalized care, remote patient monitoring, and chronic disease management outside the conventional healthcare facilities. Increased adoption of the AI-powered wearable devices, virtual assistants, and connected health platforms facilitates real-time health tracking with early intervention, which reduces hospital visits.

Historical Context

The US AI-enabled medical devices market is significantly influenced by the rise in need for diagnostics, precision, automation in healthcare systems, and personalized treatment plans. The demand for AI-enabled medical devices in the US is witnessing an upsurge owing to advancements in the form of computer vision, machine learning, and predictive analytics.

Opportunities are surging in verticals such as surgical robotics, remote monitoring, and diagnostics. The ongoing trends depict high-level adoption of AI-driven tools in cardiology, radiology, and patient management. However, challenges persist in the form of data privacy concerns, delays in regulatory approvals, and incompetent system integration.

How are Various Mergers & Acquisitions shaping the US AI-enabled Medical Devices Market?

The US AI-enabled medical devices market is witnessing mergers & acquisitions at a moderate level due to the desire for enhancing technological capabilities and gaining a competitive advantage on the part of market players. In April 2025, a Mirion Medical company called Sun Nuclear completed the acquisition of Oncospace – an AI-powered and cloud-based radiation oncology software provider. The US FDA does regulate the above-mentioned solutions under its Software as a Medical Device (SaMD) framework. The focus is on performance transparency, risk management, and clinical validation for the AI-based tools.

Report Scope

Key Developments

The US AI-enabled Medical Devices market is witnessing a significant organic and inorganic expansion. Some of the key developments include –

• In March 2025, GE HealthCare announced that it had entered into collaboration with NVIDIA for advancing autonomous diagnostic imaging by making use of physical AI. This collaboration does integrate the latter’s AI computing with the former’s imaging expertise for developing next-generation tools.

• In September 2024, Abbott announced that it had entered into a partnership with iCardio.ai for developing AI imaging solutions for improving cardiovascular diagnostics.

• In February 2024, Philips introduced the AI-enabled CT 5300 system at ECR 2024. The system has been designed for improving diagnostic confidence, streamlining workflows, and improving patient care across interventional, cardiac, and screening applications.

Leading Players

The US AI-enabled Medical Devices market is highly competitive, with a large number of service providers globally. Some of the key players in the market include:

• Siemens Healthineers
• GE HealthCare
• Philips Healthcare
• Medtronic
• Johnson & Johnson (Ethicon)
• Stryker
• Canon Medical Systems
• Zimmer Biomet
• Abbott Laboratories
• Boston Scientific
• Exo Imaging Inc.
• Empatica
• Aidoc
• Clew Medical
• Eko Healthcare
• Paige AI Inc.
• Others

These firms apply a plethora of strategies to enter the market, including innovations, mergers and acquisitions, and collaboration. The US AI-enabled medical devices market is shaped by the presence of diversified players that compete based on product innovation, vertical integration, and cost efficiency.

The US AI-Enabled Medical Devices Market is segmented as follows:

By Component

• Software
• Hardware
• AI-enabled Medical Devices
• Smart Wearables
• Services

By Technology

• Machine Learning
• Deep Learning
• Supervised
• Unsupervised
• Natural Language Processing (NLP)
• Computer Vision
• Context-Aware Computing

By Therapeutic Area

• Cardiology
• Neurology
• Radiology
• Oncology
• Mental and Behavioral Health
• Ophthalmology

By End-use

• Hospitals & Clinics
• Diagnostic Centers
• Ambulatory Surgical Centers (ASCs)
• Home Care Settings

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