Commercializing AI in medical technology: Challenges & opportunities

Artificial intelligence is no longer an emerging technology in health care— it is the present. As of July 2025, the FDA has authorized more than 1,250 AI-enabled medical devices for clinical use in the United States, representing a dramatic acceleration from 950 devices just one year earlier. Despite this regulatory progress, a critical paradox persists. Hundreds of AI algorithms are published annually in peer-reviewed journals, yet only a fraction is translated into clinical practice.

The difference between algorithmic success and commercial success lies not in technical sophistication alone, but in strategic commercialization. This reality was underscored by the FDA's January 2025 guidance on lifecycle management for AI-enabled devices, which establishes clearer expectations for manufacturers at every stage, from development through real-world deployment. Industry professionals who understand these pathways—and the evolving landscape of funding, regulation, reimbursement, and guideline integration—are uniquely positioned to accelerate innovation.

This article examines the current commercialization environment for medical AI technologies, exploring both the infrastructure that enables success and the persistent barriers that continue to impede adoption, with a specific focus on practical examples and developments that define the industry.

The clinical opportunity: Why medical AI matters

The foundational case for medical AI remains compelling. Consider abdominal aortic aneurysms (AAA), a condition affecting approximately 5% of the global population, which frequently go undetected until rupture, and carries mortality rates approaching 80%. In 2024, Chiu and colleagues demonstrated a deep learning algorithm that enables nurses with no formal ultrasound training to detect AAA with 100% sensitivity and 97.8% specificity, completing diagnostic imaging in an average of 37 seconds, with diagnostic quality comparable to that of ultrasound-trained physicians. This technology addresses a genuine clinical bottleneck: global shortages of trained ultrasound technicians, particularly in low-resource settings.

Yet this validated technology's path to widespread adoption illustrates the challenge of commercialization. The algorithm alone—no matter how accurate—cannot create clinical impact. It requires institutional support, regulatory navigation, reimbursement mechanisms, and integration into clinical practice guidelines. These elements constitute the commercialization infrastructure upon which innovation depends.

Similarly, in diagnostic imaging, AI applications continue to extend beyond radiology. A 2025 systematic review examining AI-assisted fracture diagnosis on radiographs found that AI systems achieved pooled sensitivity and specificity above 90%, comparable to radiologist performance, with reader studies confirming that AI assistance improved radiologist sensitivity by 6-8% without loss of specificity. Yet despite this demonstrated value, most of these tools lack dedicated billing codes or clear reimbursement pathways, which are fundamental requirements for sustainable clinical deployment in fee-for-service healthcare systems.

The commercial blueprint: Learning from success

Several companies have successfully navigated the complete commercialization pathway, providing a model for emerging ventures. A prominent example is Viz.ai, a US-based neuroimaging company founded in 2016 that has become a benchmark for AI medical device commercialization. Viz.ai's trajectory reveals the essential ingredients for successful translation:

Multidisciplinary team and robust algorithm development: Viz.ai assembled a team combining clinical expertise, software engineering, and regulatory knowledge. The company published extensively in peer-reviewed journals, establishing scientific credibility that attracts both investors and clinicians. By 2024, the company generated $48.8 million in revenue with a team of 325 employees, a scale that required methodical investment in product quality.

• Integrated regulatory approach from inception: Rather than treating FDA requirements as an afterthought, Viz.ai implemented quality management systems aligned with International Organization for Standardization (ISO) certifications from the outset. This approach, although initially resource-intensive, ultimately accelerated regulatory clearance by eliminating the need for costly retrofitting of quality frameworks. The company obtained FDA clearance for multiple AI algorithms that analyze medical imaging, including CT scans, electrocardiograms, and echocardiograms.
• Health technology assessment to demonstrate value: Viz.ai commissioned a preliminary health technology assessment demonstrating that AI-assisted ischemic stroke detection could save £11 million annually in the UK by identifying strokes that would otherwise be missed. This quantitative evidence of clinical and economic value proved critical in attracting investment; the company ultimately raised more than $290 million to develop and scale its products.
• Coordinated engagement with payers: Viz.ai worked directly with the U.S. Centers for Medicare & Medicaid Services to establish a billing code (CPT code 93054 and successor codes) for AI-based ischemic stroke detection, with an initial reimbursement rate of $1,040 per patient. This pathway represents a rare success story in medical AI reimbursement—most devices launched since 2020 still lack Medicare coverage.
• Persistent stakeholder engagement: Sustained engagement with guideline-setting organizations was critical to Viz.ai's success. The company collaborated with professional societies to encourage the integration of AI-assisted stroke detection into clinical practice guidelines, thereby legitimizing the technology and facilitating clinical adoption.

This commercialization pathway, while demanding significant capital and institutional expertise, has become the implicit standard against which investors evaluated AI medical device ventures.

The funding landscape: Capital requirements and strategic positioning

Despite increased venture capital interest in health care, funding for medical AI remains concentrated among companies with clear commercial narratives. Private investors increasingly look beyond algorithmic accuracy to assess clinical impact potential and the viability of revenue generation.

For rare disease applications—such as AI-enhanced AAA screening affecting less than 5% of the population—funding challenges intensify. However, strategic positioning can mitigate this concern. The case for niche clinical markets has strengthened: investors recognized that smaller, highly specialized markets face less competitive pressure than saturated segments such as general oncology or cardiology screening, where numerous competitors vie for market share. Additionally, technologies developed for specific applications often demonstrate adaptability. AI-enhanced ultrasound guidance for AAA screening, for example, can be adapted to other vascular conditions—carotid disease, peripheral artery disease, and venous disease—significantly expanding the addressable market.

Health technology assessment emerges as a practical tool for securing funding. By systematically evaluating cost-effectiveness, safety, and sociocultural implications, ventures demonstrate their value proposition in quantifiable terms that resonate with institutional investors. This evidence becomes particularly valuable when approaching Series A funding rounds. As of 2025, investors prioritize ventures that have identified CPT codes or ASC reimbursement pathways, developed budget-impact models, and engaged in early discussions with payers—clear de-risking factors that signal management sophistication.

Regulatory pathways: FDA modernization

The FDA's regulatory environment for AI medical devices has undergone substantial evolution, with three pivotal developments in the first half of 2025 reshaping the landscape:

• AI-Assisted Scientific Review: In May 2025, the FDA announced the completion of its generative AI pilot program for scientific reviewers and directed all FDA centers to deploy AI-assisted review tools immediately, with full integration targeted for June 2025. FDA Deputy Director Jeremy Walsh characterized the technology as a "game-changer," noting that tasks previously requiring three days of reviewer time could be completed in minutes. While implementation details remain limited, this deployment could accelerate 510(k) review timelines, a critical factor affecting product launch schedules.
• Lifecycle Management Guidance: In January 2025, the FDA published draft guidance titled "Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations," which addresses a persistent challenge in AI device regulation. The guidance clarifies expectations for continuous monitoring, predetermined change-control plans, and real-world performance evaluation, acknowledging that AI systems inevitably evolve as they encounter diverse clinical datasets and patient populations.
• Predetermined Change Control Plans (PCCP): This increasingly standard requirement allows manufacturers to submit anticipated modifications alongside initial approval, specifying how changes will be implemented and monitored. The precedent was established by Caption, an AI-powered cardiac ultrasound software approved via the De Novo pathway in 2020, specifically because the manufacturer included a robust PCCP. This regulatory flexibility has become essential for maintaining FDA compliance as AI models are retrained on new data.

Despite these improvements, substantive barriers remain. FDA clearance via the 510(k) pathway still requires demonstrating substantial equivalence to an existing device challenge when technologies are genuinely novel. An analysis of devices approved through September 2025 revealed that 96.7% followed the 510(k) pathway, 2.9% pursued De Novo review, and 0.4% sought PMA (Premarket Approval). This reliance on 510(k) equivalence poses a risk: the use of "predicate" devices to justify clearance may result in insufficient scrutiny of genuinely novel AI approaches.

Practical regulatory strategy

Early engagement with the FDA through the Q-Submission process has become standard practice. This pre-submission mechanism allows companies to receive written feedback on proposed regulatory strategies, potentially identifying predicate devices or pathway options before beginning formal submissions. For developers planning 2026-2027 launches, engaging the FDA's Office of Strategic Policy (now emphasizing AI-specific resources through the Medical Device Tools Program) streamlines the pathway and reduces approval timelines.

The reimbursement crisis: The stumbling block to scale

Despite regulatory progress, reimbursement remains the critical barrier to widespread AI adoption. As of April 2025, only a handful of FDA-cleared AI devices have achieved Medicare coverage, creating what one observer termed a "wild west" of ad-hoc payment arrangements.

The reimbursement challenge stems from a fundamental structural issue: existing billing codes—CPT codes developed over decades ago for manual diagnostic and procedural services—do not accommodate algorithm-driven services. Modifying these codes or creating new ones requires buy-in from multiple stakeholders, including Medicare Administrative Contractors, commercial insurers, clinicians, and patient advocacy groups. The process is neither standardized nor predictable, and ventures rarely complete it within 3-5 years.

Successful reimbursement models

A 2025 survey identified several breakthrough applications that have achieved reimbursement status:

• HeartFlow FFR-CT Analysis: This non-invasive fractional flow reserve analysis tool achieved coverage by 5 MACs, representing thirty-eight states, as of early 2025, with CPT code 75580 established as a Category I code. HeartFlow's success was built on multiple published health economics studies demonstrating improved patient outcomes and cost-effectiveness compared to invasive coronary angiography. The company's persistent engagement with both MACs and CMS supported the development of coverage policies specific to FFR-CT analysis.
• Koios Medical Breast and Thyroid Ultrasound AI: Koios Medical's AI-enhanced quantitative ultrasound tools were reimbursed under Category III CPT codes 0689T and 0690T, introduced in 2022. These codes, while temporary and underutilized, provide reimbursement 30% higher than standard ultrasound codes for professional fees and 10% higher for technical fees. This premium reflects the additional value delivered by AI-assisted diagnosis.
• Radiologic AI with MAC Coverage: Numerous radiology AI applications have secured Local Coverage Determinations (LCDs) from individual MACs, a significant milestone indicating that payers have deemed "reasonable and necessary" for coverage—even without national Medicare policies.

These successes share common elements: strong clinical evidence of improved outcomes, demonstrated cost-effectiveness, persistent engagement with payers, and professional society advocacy. Notably, none achieved reimbursement quickly; each represents years of coordinated effort between industry, clinicians, and health systems.

Emerging reimbursement pathways

Two new mechanisms introduced or expanded in 2025 offer alternatives to traditional CPT code pathways:

Transitional Coverage for Emerging Technologies (TCET): Announced by CMS in 2024 and clarified in 2025, TCET provides temporary national Medicare coverage for technologies designated as FDA Breakthrough Devices, with a target time of six months from FDA market authorization to final coverage determination. This pathway may enable ventures to expedite reimbursement decisions, although selection is competitive (CMS expects up to five nominees quarterly).

Health Tech Investment Act (S. 1399): Introduced April 9, 2025, by Senators Mike Rounds (R-SD) and Martin Heinrich (D-NM), this bipartisan proposal would create a dedicated payment pathway for Algorithm-Based Healthcare Services (AHBS) defined as FDA-cleared or -approved services using AI, machine learning, or similar software. The bill proposes assigning AHBS to a modern technology ambulatory payment classification with a transitional period of at least five years, providing predictability that could accelerate both innovation and adoption. While the bill had not progressed through Committee Finance as of December 2025, its bipartisan sponsorship and alignment with recommendations from the Senate AI Working Group suggest potential for advancement in 2026.

Implementation and integration into clinical practice

Securing regulatory approval and reimbursement represents only partial success. Clinical integration, embedding AI tools into routine workflow and guideline-endorsed practice, requires sustained effort with professional societies and health care systems.

The challenge is illustrated by the AAA screening case referenced in the source document. Despite guideline recommendations, AAA screening remains inconsistently performed, with aneurysms often detected incidentally rather than through active screening. Two professional societies offer discordant guidance: the Society for Vascular Surgery (SVS) recommends ultrasound screening for men and women aged 65-75 with smoking history or family history, while the US Preventive Services Task Force (USPSTF) restricts recommendations to men aged 65-75 with smoking history, citing cost concerns associated with screening a relatively low-prevalence condition.

AI-enhanced AAA screening could resolve this discordance by reducing screening costs and increasing efficiency, potentially supporting broader guideline adoption. However, achieving this integration requires explicit engagement with both SVS and USPSTF guideline committees, submission of evidence supporting AI-enhanced approaches, and coordination with professional society leadership to prioritize guideline revision. Few ventures dedicate sufficient resources to this effort, yet clinical guideline integration often determines whether technologies achieve broad adoption.

Emerging challenges: Algorithmic bias and real-world performance

As AI medical devices scale from controlled clinical trial settings to diverse real-world populations and healthcare systems, new challenges have emerged.

Algorithmic Bias in Imaging: A systematic review published in the American Journal of Roentgenology in 2025 documented algorithmic bias across multiple AI imaging applications, with particularly concerning findings in breast and chest imaging datasets. Models trained predominantly on specific demographic groups exhibit degraded performance when applied to underrepresented groups—a pattern that raises both safety and equity concerns. The FDA's January 2025 guidance acknowledged this issue, requiring manufacturers to address demographic representation in training datasets and to monitor performance across subgroups. Algorithm transparency and analytics are also ways to avoid bias. However, implementation standards remain inconsistent across vendors. Bias is a pervasive problem across all medicine and can be exacerbated within AI algorithms if guidelines are not put in place.

Real-World Performance Monitoring: In September 2025, the FDA issued a Request for Public Comment on "Measuring and Evaluating Artificial Intelligence-enabled Medical Device Performance in the Real-World," signaling regulatory intent to establish standards for post-market performance evaluation. This reflects growing recognition that laboratory validation—essential for pre-market approval—does not always predict field performance. Healthcare systems implementing AI devices report occasional discrepancies between vendor-reported accuracy rates and observed clinical performance, particularly when devices encounter data distributions not well-represented in training cohorts.

These challenges underscore the ongoing importance of human-in-the-loop clinical workflows. The most successful implementations maintain expert physician oversight and preserve clinical judgment as the final decision-making authority, rather than treating AI as an autonomous diagnostic system.

Building the organization: Lessons for founders

Ventures successfully commercializing medical AI share several organizational characteristics:

Embedded Regulatory and Clinical Expertise: Rather than treating regulatory affairs and clinical consultation as external functions, successful companies embed regulatory strategy and clinical input into core product development. This reduces late-stage surprises and accelerates approval timelines.

Multidisciplinary Leadership: Founding teams that combine MD/DO clinicians, software engineers, regulatory specialists, and health economics experts outperform teams with single-domain expertise. This diversity prevents siloed thinking and ensures prompt attention to commercialization requirements.

Early Payer Engagement: Rather than completing product development and then approaching payers, successful companies initiate payer discussions during the development phase, securing informal feedback on reimbursement likelihood and required evidence. This "fail-fast" approach prevents ventures from developing products likely to encounter insurmountable reimbursement barriers.

Health System Partnerships: Ventures that establish relationships with academic medical centers and integrated health systems during development benefit from clinical validation environments, user feedback, and potential early adopter status post-launch. These partnerships accelerate evidence generation and support regulatory submissions.

Patient and Professional Society Advocacy: Companies that engage patient advocacy organizations and professional societies establish early channels for guideline integration and support clinical adoption. This investment begins years before regulatory approval but pays dividends when guidelines must be updated post-launch.

Conclusion: The maturation of medical AI commercialization

Medical AI has transitioned from experimental innovation to mainstream health care technology. This maturation is reflected in increasingly sophisticated regulatory frameworks, emerging reimbursement pathways, and institutional investor expectations. The era of garage-based algorithm development followed by a surprise FDA submission is past.

Industry professionals responsible for clinical implementation of medical AI—whether in regulatory affairs, clinical operations, reimbursement strategy, or product development—must now operate as integrated teams navigating a multifaceted landscape. Algorithmic accuracy, while necessary, is no longer sufficient for commercial success. Instead, ventures must demonstrate:

• Clinical evidence of improved patient outcomes
• Health economic value in quantitative terms
• Compliance with evolving quality and regulatory frameworks from project inception
• Realistic pathways to reimbursement requiring years of systematic payer engagement
• Integration into clinical practice guidelines through professional society partnerships
• Real-world performance monitoring strategies addressing algorithmic bias and population diversity

The companies and professionals who successfully navigate this environment will be those who view commercialization not as a phase following algorithm development, but as an integrated strategy shaping product development from conception through sustained clinical implementation.

As the FDA noted in its January 2025 lifecycle guidance, "The successful integration of AI into healthcare requires not innovation in algorithm alone, but innovation in supporting infrastructure of validation, regulation, economics, and clinical adoption." This reframing—from technological achievement to systemic integration—defines the maturation of medical AI commercialization.

Partha Anbil is at the intersection of the Life Sciences industry and Management Consulting. He is currently SVP, Life Sciences, at Coforge Limited, a $1.7B multinational digital solutions and technology consulting services company. He held senior leadership roles at WNS, IBM, Booz & Company, Symphony, IQVIA, KPMG Consulting, and PWC.

Kelli Bravo is the VP of Healthcare and Life Sciences at Coforge where she is focused on AI digital transformation and engagement strategies that build relationships, simplify operations, and improve healthcare delivery. She most recently was the global leader of Pega’s Healthcare and Life Sciences business and prior to that was a VP at Centene responsible for the go-to-market strategy for their care management enterprise solutions.