A roadmap for AI-driven Software as Medical Device success in the U.S.
Securing FDA approval for innovative software medical device solutions is a monumental achievement, often requiring years of rigorous testing and data collection, and usually seen as the ultimate milestone. But what is the reality beyond the FDA green light?
Entering the U.S. healthcare market with an innovative AI-driven Software as Medical Device (SaMD) requires much more than regulatory clearance. From proving clinical value to differentiating in a saturated market, companies face different challenges to navigate clinical adoption.
In this blog, we discuss actionable strategies to turn approval into widespread adoption in the U.S. healthcare market.
Key challenges and strategies for software medical device adoption
1. Seamless integration into existing clinical workflows
Healthcare providers already rely on a mix of complex systems within hospital IT infrastructure, including industry-standard systems like PACS, Picture Archiving and Communication System, where imaging data is stored. Solutions that fail to integrate easily into PACS or disrupt existing routines can hinder adoption.
To ensure these systems don’t feel like ‘add-ons’ to radiologists, it’s crucial to design software as medical device solutions that are compatible with existing clinical systems. By integrating SaMD with PACS, the system can automatically retrieve relevant imaging data, process it in real time, and deliver actionable results directly into the clinician’s dashboard.
For solutions like QP-Prostate, integration should also account for the subsequent steps in the clinical workflow. Results from lesion segmentation can serve as input for the fusion biopsy processes. This level of interoperability, when paired with the right technical support and training, can accelerate early adoption. Moreover, it holds the potential to reduce hospital costs while reinforcing diagnostic accuracy and improving patient outcomes.
2. How to build clinical trust for software as medical device adoption
The ‘black box’ effect of AI or Machine Learning-driven SaMD solutions often fosters skepticism among clinicians, preventing them from adopting the technology.
The foundation of building trust lies in transparency and education. Companies must provide accessible explanations of how their algorithms work, backed by clinical validation through peer-reviewed studies. Educational programs, such as workshops and training, play a key role in this process. Additionally, partnering with medical societies enhances the technology’s credibility and promotes broader acceptance within the healthcare community.
However, individual adoption is just the beginning, the real impact comes from influencing clinical guidelines. As more clinicians recognize the value of AI-based solutions, there is a growing opportunity to integrate them into standard protocols of clinical practice, ultimately improving clinical decision-making processes and enhancing patient care.
3. Proving the value of AI-Driven software as medical devices in Real World Datasets
While pre-market clinical trials are essential for regulatory approval, they often cannot fully capture the complexity of real-world clinical environments. AI-driven software medical device solutions must demonstrate effectiveness beyond controlled trial conditions. However, gathering real-world data post-market can be challenging due to the variability in clinical practices, patient populations, and healthcare infrastructure.
Real-world evidence highlights how AI-driven SaMD improves patient outcomes, enhances operational efficiencies, and potentially reduces healthcare costs – critical factors to clinicians and healthcare administrators. To generate this evidence, companies should collaborate with healthcare providers and institutions to conduct studies that track the effectiveness of their tools in routine clinical use focused on accuracy of diagnoses, time-to-treatment, and improvements in patient care. Actively publishing RWE results in peer-reviewed journals and presenting findings at medical conferences will help strengthen ties with the medical community.
4. Securing reimbursement and coverage for algorithm-based software as medical devices
Demonstrating RWE is critical not only for validating the effectiveness of SaMD solutions but also for establishing their clinical utility. By showcasing how these technologies optimize costs, improve workflows, and enhance patient outcomes, companies can build a robust case for their inclusion in reimbursement systems.
In parallel, company efforts should focus on aligning with existing CPT codes where possible or advocating for the creation of new ones.
Once the appropriate CPT code and evidence of clinical utility are established, engaging with payors to secure coverage becomes possible. It is beneficial to direct sales efforts and engage with payors in the same regions, as providers can receive reimbursement, which facilitates technology adoption and, in turn, drives sales.
5. The need for competitive differentiation in the market
In a crowded market of medical imaging AI solutions, differentiation is essential for success. Companies that go beyond diagnosis and detection to unlock the predictive potential of imaging data can carve out a distinct niche. While traditional AI tools excel at pattern recognition, identifying anomalies or critical findings on scans, there is an opportunity to advance further by providing actionable insights that empower clinicians to make forward-looking decisions.
A key strategy is leveraging imaging biomarkers to predict disease progression, treatment response, or recurrence likelihood . For example, advanced biomarkers can assess tumor heterogeneity to help oncologists anticipate therapy outcomes or quantify disease burden over time to guide early interventions. This proactive healthcare management not only sets solutions apart but also aligns with the broader shift toward personalized medicine.
Quibim exemplifies this approach by combining advanced imaging biomarkers with predictive analytics to enhance personalized care. This model illustrates how innovation in medical imaging AI can lead to greater adoption and clinical relevance.
6. Green Field Opportunity: Comparing Selling to OEMs, Academic Medical Centers, and Radiology Groups/Imaging Centers
While clearly demonstrating the clinical and operational value of the technology, success requires targeted strategies for each customer segment. AI-driven SaMD solutions offer unique opportunities in distinct market segments: OEMs, academic medical centers, and radiology groups. Each target market presents advantages, but the pathways to adoption vary significantly. Below is a streamlined comparison:

Conclusion
Success in the U.S. healthcare market demands a holistic strategy that balances rigorous regulatory compliance, robust real-world evidence and differentiation, pursuit of reimbursement and well-defined sales strategy.
By ensuring these elements are addressed in tandem, AI-driven software medical device solutions can reshape healthcare delivery and solidify its place in everyday medical practice, enhancing better patient outcomes and personalized medicine.
Are you ready to be part of this transformative change and shape the future of U.S. healthcare?