Software as a Medical Device (SaMD) Market

The global Software as a Medical Device (SaMD) Domain was worth of USD 29.33 billion in 2025 and it is expected to reach USD 34.7 billion by the end of 2032.

The global Software as a Medical Device (SaMD) domain is rapidly evolving as healthcare providers increasingly adopt standalone software solutions that perform medical functions independent of traditional hardware-based medical devices. Beyond being adjuncts to hardware, these solutions deliver diagnostics, monitoring, therapeutic support and clinical decision-making through algorithms, cloud platforms and intelligent software modules. This shift opens up new value streams while transforming care delivery—from telehealth to in-hospital AI-driven diagnostics. As digital health infrastructure strengthens and regulatory clarity improves, the SaMD paradigm is becoming a focal point for medical software innovation and investment. The Global Software as a Medical Device (SaMD) market is undergoing rapid expansion as healthcare systems integrate advanced software into diagnostic, monitoring and therapeutic workflows. As Software as a Medical Device in healthcare becomes essential to digital transformation, regulators such as the FDA Software as a Medical Device program and India’s CDSCO SaMD guidelines are shaping innovation, compliance, and global market access.

Among the key drivers is the accelerating integration of artificial intelligence (AI) and machine-learning (ML) into SaMD:

Such algorithms enable early disease diagnosis, personalized treatment plans and continuous patient monitoring. Advances in algorithms (deep learning, reinforcement learning) enabling tasks previously only achievable by humans (e.g., pattern recognition in images, predictive analytics). As of 2024, there are approximately 950 FDA-approved AI/ML-enabled medical devices authorized by the FDA, out of which 723 in radiology. Aidoc developed AI algorithms that detect urgent findings on CT scans (e.g., intracranial hemorrhage, pulmonary embolism) and received FDA clearance for such tools. The company’s algorithm for intracranial hemorrhage detection on non-contrast brain CT achieved ~93 % sensitivity and ~97 % specificity in one study. Eko’s digital stethoscope plus AI software platform can detect heart murmurs, atrial fibrillation, and reduced ejection fraction. In one published trial (in UK primary care) the AI-enabled stethoscope identified reduced ejection fraction in an obstetric population more than traditional screening.

Firms that embed AI/ML capabilities in their SaMD offerings can claim higher clinical value, better workflow integration, and data-driven outcomes. Having AI/ML in SaMD means you must design for lifecycle updates, model drift, real-world performance monitoring — which raises the bar but also raises entry-barriers for less capable competitors. Health systems and payers increasingly demand evidence of improved outcomes. AI/ML-enabled SaMD can generate that evidence (early detection, reduced readmission, improved workflow) and therefore supports strategic positioning. Regulatory clarity continues to accelerate adoption. The FDA Software as a Medical Device framework — supported by its AI/ML Action Plan — provides structured pathways for algorithm-based software approvals. In India, the Software as a Medical Device CDSCO classification under the Medical Device Rules, 2017 strengthens compliance expectations.

The rising prevalence of chronic conditions and the demand for remote care and outcome-based models further fuel adoption. For example, regulatory bodies such as the U.S. Food & Drug Administration (FDA) have published an “AI/ML SaMD Action Plan” to help govern adaptive software in healthcare. According to study, global burden of chronic disease cost reaching US$ 47 trillion by 2030, emphasising the large financial pressure that chronic conditions place on health systems.

On the flip side, a primary restraint remains the complex and evolving regulatory landscape:

Requirements for pre-market review of adaptive algorithms, uncertainty over reimbursement pathways and variable global regulatory harmonisation pose adoption challenges. Meanwhile, a significant opportunity lies in the value-based care shift: healthcare systems increasingly reward outcomes rather than volume, driving interest in SaMD solutions that can demonstrate improved clinical results, reduced readmissions or cost-savings. In underserved geographies, digital workflows offer a leap-frog opportunity for software-first medical solutions.

North America remains a front-runner in SaMD adoption thanks to its advanced technology infrastructure, supportive regulatory frameworks and investment ecosystem. Europe follows closely, driven by digital health initiatives and government policy support for AI-enabled healthcare. In Asia-Pacific, markets such as Japan are gaining traction: for instance, the Japanese SaMD market was reported at approximately US $17.24 million in 2025 and expected to grow significantly through the forecast period. Moreover, emerging economies are showing increased willingness to adopt remote monitoring and mobile-first SaMD platforms, making them promising markets for new entrants willing to localise solutions. Regulatory changes in regions such as India underscore the growing global attention to software-driven medical technologies. In terms of deployment models, cloud-based SaMD solutions dominate, supported by their scalability, connectivity and real-time update potential. According to industry data, cloud-hosted solutions accounted for nearly 65% of revenues in 2025 in one study. By application, screening & early detection is a large slice—accounting for around 37.34% of the market in 2025 in one analysis—while chronic condition management is ramping fast, with a reported CAGR of ~43.54% to 2032.

Notable Market Player Developments

• Royal Philips (Philips) unveiled new AI-enabled systems and cloud-based radiology software at the 2025 European Congress of Radiology, emphasising intelligent workflows and sustainable solutions in imaging.
• Siemens Healthineers entered a 10-year Value Partnership (announced April 3, 2025) with Tower Health in the U.S., committing digital tools, software automation and analytics across imaging workflows—highlighting the growing role of SaMD in clinical operations.
• In a broader strategic move, Siemens AG announced the acquisition of U.S.-based science-software company Dotmatics for ~US $5.1 billion (Apr 2 2025), signalling intent to deepen its software-led footprint in life sciences and medical software.
• Health-tech M&A surged in 2025, with 102 deals in H1 2025—near the full-year totals of prior years—driven in part by software-centric health-tech companies merging or being acquired.

Analyst comment:

As the SaMD industry shifts from hardware-adjunct software to fully autonomous, clinically-validated software platforms, decision-makers at the C-suite level must recognise that winning the next wave of medical device value will require software-first strategies. Companies that build robust AI/ML models, ensure regulatory readiness and embed their platforms into clinical workflows will secure lasting competitive advantage. Investors and strategic buyers are actively consolidating around software capabilities, making now a pivotal moment to define product-roadmaps, reimbursement pathways and regional go-to-market models.

Market Segmentation

By Function / Software Type

• Diagnostic SaMD (Software that identifies disease/conditions (e.g., image analysis, pattern recognition).
• Monitoring & Remote Patient Monitoring (RPM) SaMD (Continuous or periodic monitoring of vitals, biomarkers, adherence)
• Clinical Decision Support (CDS) SaMD (Decision aids for clinicians (risk scores, triage).
• Therapeutic / Digital Therapeutics (DTx) SaMD (Software that delivers therapy (behavioral, rehabilitative).
• Workflow & Operational SaMD (Scheduling, image routing, AI triage, reporting automation)

By Application / Therapeutic Area

• Radiology / Imaging (CT, MRI, X-ray)
• Cardiology (ECG analysis, Echo Interpretation)
• Neurology (Stroke, EEG analysis)
• Oncology (Tumor Detection, Progression Tracking)
• Endocrinology / Diabetes (Glucose Monitoring Analytics)
• Respiratory (COPD, Asthma)
• Mental Health (Assessment, CBT delivery)
• Infectious Diseases / Sepsis Prediction
• Others

By Regulatory Class / Risk Level

• Low-to-Moderate Risk (e.g., CDS, Non-diagnostic tools)
• High Risk (Diagnostic/therapeutic decision makers)
• Adaptive AI/ML SaMD

By Deployment Model

• Cloud-hosted / SaaS (Multi-tenant, Central Updates)
• On-premise / Edge (Data privacy / Latency sensitive)
• Hybrid

By Technology Stack / Capability

• AI/ML-enabled (deep learning, classical ML)
• Rule-based / Algorithmic
• Connected (IoT + wearables)
• Blockchain / Secure Audit-trail features

By End-User / Buyer Persona

• Hospitals & Health Systems
• Clinics / Ambulatory Care
• Payers / Insurance
• Patients / Consumers
• Diagnostic Labs / Imaging Centers
• Government / Public Health Agencies

By Distribution Channel

• Direct sales (enterprise sales teams)
• OEM / Embedded (software bundled with medical devices)
• Partnerships with EMR vendors / system integrators
• Marketplaces / App ecosystems (EHR app stores)

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East and Africa

Market Players

• Philips (Healthcare)
• Siemens Healthineers
• GE HealthCare
• Canon Medical Systems
• Fujifilm Healthcare
• Agfa HealthCare
• Roche Diagnostics
• Medtronic
• Abbott
• Aidoc
• ai
• Eko Health
• AliveCor (Kardia)
• Caption Health
• Arterys
• Lunit
• HeartFlow
• iRhythm Technologies (Zio)
• ai
• Butterfly Network
• Tempus
• Paige (Paige.ai)
• Biofourmis
• Pear Therapeutics
• Omada Health
• Kaia Health
• Corti
• Ultromics
• Kheiron Medical
• ai
• Oxipit
• VUNO
• Riverain Technologies
• Imagen Technologies
• Enlitic
• Suki
• Health Catalyst
• Concerto HealthAI
• PathAI
• Niramai Health Analytix
• SigTuple
• Zebra Medical Vision
• Others