US AI-Enabled Medical Devices Market 2026 – 2035

It is estimated that the market size of the US AI-enabled medical devices market will grow in the period between 2026 and 2035 to USD 4.82 billion in 2025, and the same will increase to USD 5.58 billion in 2026, and to approximately USD 18.94 billion in 2035, with an annual CAGR of 15.8% within the years 2026-2035. This market is expanding due to more FDA-approved AI-powered diagnostic and therapeutic devices, the rise in demand for precision medicine and individualized treatment methods, the lack of healthcare professionals that necessitates automation, the large amount of medical imaging that needs handling with AI assistance, and the large investment volume in healthcare AI startups.

Market Highlight

• The market share in 2025 based on the Northeast region had a 38% market share of the US AI-enabled medical devices market.

• The growth rate in the West region is 17.4% between the years 2026 and 2035.

• The radiology and medical imaging AI had occupied over 42% of the market share in 2025 by product type.

• By technology, the deep learning segment will have the greatest CAGR of 17.2%.

• Application-wise, the highest CAGR of 16.8% is projected to fall on the oncology segment in the lifespan of the projection, 2026-35.

• By end user, 58% of market share in 2025 was in the hands of hospitals.

• The FDA gave its approval to 692 AI-enabled medical devices in 2024, including 58% of all medical applications in radiology, evidence of regulatory pathway maturation.

Significant Growth Factors

• Exponential Growth in FDA Approvals and Regulatory Clarity:

The implementation of regulatory guidelines and a faster rate of AI-enabled medical device clearance by the FDA generates a sense of security in the market, allowing business implementations while maintaining safety and efficacy. As of December 2024, the FDA had approved 692 AI-enabled medical devices, up from 171 in 2020, a 304% rise over a four-year period, with the pace of approval accelerating to 12-15 monthly in 2024 (Kumar 4). Radiology applications are the most dominant, with 401 approvals (58%) that entail mammography screening algorithms, chest X-ray interpretation algorithms, CT head hemorrhage algorithms, and MRI lesion algorithms. cardiology applications have 128 approvals (18.5%), inclusive of ECG analysis algorithms, echocardiography interpretation algorithms, and cardiac imaging assessment algorithms.

The Software as a Medical Device framework created by the FDA in 2017 and updated in 2021 offers specific regulatory guidance to the continued learning nature of AI, including the predefined plans to change control, which allows updating the algorithm without submitting a new 510(k) application in the cases when the changes fall within the scope of validated performance. In 2020 the Digital Health Center of Excellence was introduced, which offers regulatory advice, and 840+ pre-submission consultations have been provided, where developers are able to understand requirements to reduce the approval timelines by making a well-designed study before submitting formal applications, instead of 12-18 months. In January 2021, the FDA published the Software as a Medical Device Action Plan, an AI/ML-based plan that described the potential strategies of real-world performance monitoring, reduction of algorithm bias, and transparency in AI decision-making, which covered the major stakeholder concerns about black-box algorithms and set the basis to deploy AI responsibly. Designation of the Breakthrough Device Program of 78 products in 2024 gives priority review, interactive feedback, and faster reviews of technologies that prove to have the potential to offer more effective treatment of life-threatening conditions.

• Healthcare Professional Shortage Driving AI Adoption:

The acute deficits of radiologists, pathologists, and other medical professionals pose an urgent demand on AI-enhanced processes that enhance productivity and burnout, as well as increase the availability of expertise of specialists in underserved regions. By 2034, the Association of American Medical Colleges estimates shortages in the specialties of radiology (12,500 shortage), pathology (8,400 shortage), and primary care (48,000 shortage) as 37,800-124,000 and 48,000 physicians, respectively. The workload per imaging study varies from 15-30 minutes to evaluate complex cases, which is the average time taken by the radiologist to interpret 50-75 imaging studies per day, which forms a bottleneck in the workflow with average imaging volume currently at 3.8% per year, taking up 420 million studies in 2024. Pre-screening of studies, prioritization of critical findings and initial measurements using AI algorithms save 30-45 minutes in time spent on interpretation and help the radiologists concentrate on more complex cases that need human attention.

AI assistance enhances work-life balance, as 62% of radiologists had reported burnout in 2024 surveys, and automated triage would eradicate routine work and the need to read at the end of the day due to emergency cases. Pathology encounters the same issue of 2.4 million new cancer diagnoses each year that require tissue analysis, and the number of practicing pathologists is decreasing due to retirements and being outnumbered by new graduates, and AI digital pathology systems examining whole slide images and identifying regions of concern and quantifying biomarkers decrease the workload of pathologists by 35-50% in high volume screening. COVID-19-driven telemedicine growth generates the need to develop AI-based decision support systems that allow primary care providers to treat complicated conditions that would require specialists referral, with AI-based clinical decision support systems offering evidence-based suggestions that would decrease unnecessary specialist visits by 28% but not compromise the quality of the results.

What are the Major Advances Changing the US AI-Enabled Medical Devices Market Today?

• Predictive Analytics and Risk Stratification:

The machine learning algorithms that analyze various patient data predict negative events, disease progression, and treatment responses that help in proactive intervention and personalized care planning that optimizes results and controls costs. Prediction models of readmission that model 140+ variables such as demographics, comorbidities, vital signs, laboratory values, and social determinants identify the patients at the highest risk with AUC of 0.78-0.84, and this allows targeted approaches such as the improvement of discharge planning, home health services, and early follow-up to decrease 30-day readmission rates by 18-26% in Medicare populations where readmission penalties influence hospital reimbursement. Real-time physiological data on the ICU mortality prediction systems generate hourly risk scores, which recognize the deterioration trends 8-12 hours prior to clinical crisis, and implementing the system in ICUs has been shown to decrease ICU mortality by 12% and has facilitated goal-of-care discussions with families.

Prediction algorithms of cancer recurrence based on pathology images, genomic data, and clinical characteristics are useful to identify high-risk patients who benefit from adjuvant chemotherapy compared to low-risk patients in whom treatment is of little use and toxicity is high, enhancing treatment personalization by 15% of 5-year survival and removing unnecessary chemotherapy expenditure of $340 million per year. Predicting heart failure decompensation during ambulatory care with remote monitoring data identifies early warning signs 7-14 days before symptomatic exacerbation, which are used to adjust medication and preventive measures that result in 38% fewer hospitalizations in managed populations. Calculator of the risk of surgical complications using 60+ preoperative variables such as frailty indices and functional status predicts mortality and complication risks with 85% accuracy, informs surgical decision-making, and allows shared decision-making discussions with patients about the risk/benefit.

• Wearable Devices and Continuous Monitoring AI:

Constant physiological surveillance, disease prevention, and chronic condition management of patients outside of the clinical context through AI implemented into consumer and medical wearable devices will allow the devices to interact with their users in real life. In the Apple Heart Study, which used photoplethysmography sensors and neural networks to detect cardiac arrhythmia, 450,000 individuals had a positive result in detecting atrial fibrillation, and 84% of this detection was confirmed by clinical ECG, suggesting the ability to screen the entire population of 6.1 million Americans at risk of stroke due to undiagnosed AFib. Diabetic patients' glucose trends can be predicted 30-60 minutes ahead with 92% accuracy using continuous glucose monitoring systems that have AI-driven predictive algorithms, which prompt diabetic patients to undergo the severe events that might necessitate emergency care.

Sleep monitoring AI that analyzes accelerometry, heart rate variability, and respiratory patterns detects sleep apnea with 88% sensitivity and 85% specificity, which offers a convenient screening to 22 million Americans with undiagnosed sleep apnea related to cardiovascular disease and metabolic conditions. Fall detection algorithms that analyze accelerometer and gyroscope data detect falls with 98% accuracy to automatically raise an alarm with emergency contacts and services, and 840,000 fall detections in 2024 would allow quick responses that could lead to the prevention of serious complications in the elderly population, where falls are the leading cause of death (36,000 deaths each year). On the one hand, remote patient monitoring systems that combine data collected by several wearable devices can detect exacerbations of heart failure, COPD flares, and post-surgery complications at home with accuracy rates of 76-84%, allowing virtual care to be managed and hospitalization rates to be reduced by 32% and at the same time score an average patient satisfaction of over 4.2 out of 5.

Category Wise Insights

By Product Type

Why Radiology & Medical Imaging AI Leads the Market?

Radiology and medical imaging AI has a 42% market share in 2025 because it has a high clinical need (420 million imaging studies per year need interpretation), has an established regulatory pathway (401 FDA approvals (58% of total)), and has a well-established ROI (30-45% reduction in interpretation time). By 2024, the radiology AI industry had hit an annual revenue of 2 billion dollars, and by this time, the system was deployed in 4,200+ hospitals and imaging centers across the country.

The AI mammography with 5-13% improvement in cancer detection is responsive to 39 million screening mammograms per year as the critical audience in the population health context that could help avoid 4,800-8,200 instances of late cancer diagnosis each year and be diagnosed earlier. Algorithms to detect brain hemorrhage when implemented in 1,400 stroke centers have shortened time-to-treatment by 28 minutes, which has a direct effect on the outcome, where each 15 minute delay decreases desirable outcomes by 4%. The average hospital imaging department thru AI-pre-screening and prioritization can help the radiologists concentrate their expertise on intricate cases as the quality of work and overtime expenses saved by between 85,000-140,000 a year.

By Technology

Why Deep Learning Shows Fastest Growth?

Deep learning has the greatest CAGR of 17.2% between 2026-2035 due to its higher image analysis capabilities, capacity to handle unstructured data, and ability to improve continuously with more data. Deep neural networks that use 50-200 layers and are trained on millions of labeled images can accurately identify diabetic retinopathy, as well as classify skin cancer, pulmonary nodules, and their characterization with over 95% accuracy on a single task. Transfer learning allows using pre-trained models to fine-tune to new uses with 10,000-50,000 examples compared to 100,000+ to train in the field, speeding up development timeframes to 6-9 months instead of 18-24 months. Generative adversarial networks are used to generate realistic medical images to train the algorithm on small real-world data, whereas variational autoencoders detect the patterns of rare diseases in high-dimensional data. In 2024, the deep learning medical device market had a size of 2.18 billion, with 467 FDA-approved algorithms based on a deep learning architecture as compared to 225 based on a more conventional machine learning method.

By Application

Why Oncology Demonstrates Strong Growth?

Oncology positively reflects the new CAGR of 16.8% between 2026-2035 due to the accuracy of the paradigm of precision medicine, which demands advanced data integration, high clinical utility of better diagnosis and treatment choices, and high medical care expenses with cancer care costs over 200 billion annually. Whole slide image AI pathology tools detect cancer with 96-99% accuracy, and biomarkers such as PD-L1, HER2 and microsatellite instability predict immunotherapy and targeted therapy eligibility in 40% of advanced cancer. Radiomics algorithms using 400+ features captured in tumor imaging can predict treatment response, recurrence risk, and survival better than traditional clinical staging by a 15-25% margin, allowing individual treatment to prevent undertreatment of high-risk and overtreatment of low-risk patients.

Genomic analysis AI systems can process next-generation sequencing reads to indicate actionable mutations within 4-6 hours, and they pair patients with targeted therapies and clinical trials and implementations have led to more enrollment of patients in precision oncology trials (34). Radiation therapy planning AI is used to minimize dose delivery with minimal normal tissue exposure and tumor coverage, the time spent on planning could be reduced to 30-60 minutes to start treatment the same day instead of 4-8 hours.

Report Scope

Top Players in the Market and Their Offerings

• Google Health

• Microsoft Healthcare

• IBM Watson Health

• GE Healthcare

• Siemens Healthineers

• Philips Healthcare

• Medtronic plc

• Viz.ai

• Tempus Labs

• Paige.AI

• iCAD Inc.

• Butterfly Network

• Others

Key Developments

• In January 2025: FDA pilot AI Medical Device Pre-Certification Program was announced to expand to 45 companies, allowing the developers of algorithms to simplify the process of updating and creating new applications in the case of pre-certified developers that prove to have quality systems.

• In August 2023: Google Health collaborated with HCA Healthcare implementing AI sepsis prediction in 185 hospitals with the aim of reducing mortality due to sepsis by 15% of 270,000 US patients each year by detecting and treating it earlier.

• In March 2023: Microsoft healthcare incorporated the GPT-4 medical model in the ambient clinical documentation system used in 18,000 physician offices that showed an 85% documentation time reduction and 96% physician satisfaction.

Such strategic efforts have enabled businesses to consolidate market shares, develop new clinical uses, improve algorithm efficiency, and take advantage of new growth opportunities in the fast-changing US AI empowered medical devices market.

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

By Product Type

• Radiology & Medical Imaging AI

  • CT/MRI Analysis

  • X-Ray Interpretation

  • Ultrasound Guidance

  • Mammography Screening

• Clinical Decision Support Systems

• Patient Monitoring Devices

• Surgical Robots & Navigation

• Wearable Diagnostic Devices

• Others

By Technology

• Machine Learning

• Deep Learning

  • Convolutional Neural Networks

  • Recurrent Neural Networks

  • Transformer Models

• Natural Language Processing

• Computer Vision

• Reinforcement Learning

• Others

By Application

• Oncology

• Cardiology

• Neurology

• Radiology

• Pathology

• Orthopedics

• Others

By End User

• Hospitals

• Diagnostic Centers

• Ambulatory Surgical Centers

• Research Institutes

• Home Healthcare

• Others