Valuations must account for how contracts translate into clinical use, revenue and cash collection, alongside operating costs
Capital markets are changing how they assess South Korean medical artificial intelligence companies. A few years ago, overseas regulatory milestones, including U.S. Food and Drug Administration (FDA) authorizations and European CE marking, along with supply contracts with major hospitals, were considered key drivers of share prices. The ability of a technology company to enter a tightly regulated healthcare market was itself a scarce achievement. Increasingly, valuation depends on how extensively authorized products are used in clinical practice and how that use translates into revenue.
For remote patient monitoring, key indicators include installed beds and penetration within hospitals; for medical imaging AI, they include the number of examinations analyzed and ongoing usage volumes. Overseas operations also require scrutiny of reimbursement arrangements, claims submission, contract renewal rates and the timing of revenue recognition. The industry has reached a stage where technically validated products are judged on their ability to generate sustainable revenue in clinical practice. In a recent analysis of Seers Technology, IBK Securities highlighted continued expansion of domestic monitoring beds and the timing of revenue recognition from its United Arab Emirates (UAE) business as key variables. Because the scope of a supply contract can differ from actual operations, investors need to distinguish the proportion of installed beds covered by paid services and the duration of use at each hospital.
Coreline Soft illustrates this shift in medical imaging AI. The company said it secured 77 contracts with new customers in Germany through July this year. That represents a substantial increase from approximately 10 during the whole of last year. As low-dose computed tomography (CT) lung cancer screening for high-risk groups entered Germany’s statutory health insurance system, participating healthcare providers also expanded their screening infrastructure.
Germany’s lung cancer screening program requires more than AI that identifies an individual pulmonary nodule. It must connect multiple institutions and readers across initial and second readings, comparisons with previous images, follow-up and quality assurance. A market is taking shape in which medical imaging AI operates as part of the screening workflow. To support repeat screening, products must integrate reliably with hospitals’ picture archiving and communication systems (PACS) and reading workflows, enabling consistent comparisons between previous and current examinations.
Conversion to Clinical Use and Cash Collection Matters More Than Contracts Alone
Inclusion in screening programs also affects how medical AI companies earn revenue. While many earlier contracts involved a one-time sale of software licenses, payment models may develop around screening volumes or contracts renewed after a defined period of use. In a September 23 site-visit report on the outlook for recurring revenue, SK Securities highlighted the possibility that Coreline Soft’s revenue mix would shift from one-time sales toward recurring income. It projected that installations this year would lead to per-examination revenue next year. This remains a brokerage forecast rather than a confirmed financial result.
In the United States, indicators of existing customer usage and subscription longevity are emerging alongside new regulatory authorizations. Coreline Soft said its subscription relationship with medical imaging post-processing specialist 3DR Labs is in its fourth year, with annual usage increasing by 60%. This indicates how extensively an existing customer continues to use the product. The company also reported that overseas revenue rose 123% year on year in the first half, accounting for 61.9% of total revenue, while its operating loss narrowed by 21%. These provide early indicators for assessing whether its overseas installed base is beginning to translate into revenue.
Contract counts, installed sites, active sites and AI analysis volumes measure different things. Overseas revenue should also be separated into software license sales, per-examination charges and maintenance revenue. Commercialization can be examined across regulatory authorization, contracting, installation, actual use, reimbursement and billing, contract renewal, and additional product deployment. Authorization, contracts and installation establish the foundation for market entry, while usage, billing, renewals and further deployments indicate whether a product has become embedded in hospital operations and generates sustainable revenue.
Differences in company value emerge from both performance at each stage and conversion to the next. Investors need to examine the proportion of authorized products that secure contracts, contracted institutions that complete installation and begin operations, and AI analyses that translate into insurance claims and revenue. Usage at each institution and contract renewal rates provide a basis for assessing the durability of recurring revenue. Adoption of additional products indicates the scope for expanding business within an existing customer base.
Contract value does not automatically become revenue or cash. Hospital installation schedules, acceptance procedures, insurance claims and payment processes at governments and public institutions can delay revenue recognition and collection. For overseas contracts, actual use may begin only after local regulatory requirements, system integration and staff training are completed, extending the interval between an announcement and revenue generation. Contract size therefore needs to be considered alongside the time required to begin operations and the collection period after revenue is recognized.
The scale of recurring revenue must also be distinguished from its profitability. Higher analysis volumes can bring higher costs for cloud infrastructure, computing, hospital-specific integration, security management, clinical staff training and local customer support. Revenue per active institution and earnings per examination must be assessed alongside these operating costs to judge the quality of recurring revenue. This framework applies to Coreline Soft, remote patient monitoring providers including Seers Technology, and medical imaging AI companies in Korea and overseas. Comparable assessments of commercialization require companies to report contracted beds and hospitals separately from actual usage, insurance claims and cash collection.
Reimbursement and Hospital Integration Determine Recurring Revenue
Medical AI competitiveness depends on algorithmic accuracy, reliable use within hospital workflows and connection to established payment systems. Continued use and contract renewal by existing customers shape long-term revenue. In the United States, sustainable payment arrangements and integration into clinical practice are becoming concrete challenges for wider adoption. In July, the Journal of the American Medical Informatics Association (JAMIA) published “Translating Innovation into Payment: A.I. Reimbursement in U.S. Health Care,” which analyzed how payment for clinical AI is distributed across existing fee-for-service arrangements, additional payments and procedure codes. The analysis argued that current payment mechanisms do not adequately reflect the value of workflow integration and repeated use.
The U.S. Centers for Medicare & Medicaid Services (CMS) uses Current Procedural Terminology (CPT) and Healthcare Common Procedure Coding System (HCPCS) codes, along with New Technology Add-on Payments (NTAP), in payment arrangements for certain AI-enabled medical services. The existence of a billing code, however, does not automatically guarantee reimbursement. Actual payment depends on meeting requirements for patient indications, medical necessity, clinician qualifications, payer coverage policies and billing conditions. Assessments of a company’s prospects must therefore distinguish the availability of a code from whether an insurer pays the claim.
Insurance reimbursement and inclusion in national screening programs create a foundation for recurring use while increasing dependence on policy. Changes in covered populations, payment rates or provider eligibility can alter expected examination volumes and revenue. Growth opportunities from entry into established healthcare systems should be assessed alongside a company’s pricing power and the duration of reimbursement coverage. Revenue concentration in a particular country or program is another measure of long-term stability.
In Europe, clinical adoption and hospital system integration have been identified as major priorities for medical AI. According to “Artificial Intelligence Is Reshaping Health Systems: State of Readiness Across the European Union,” published in April by the World Health Organization’s Regional Office for Europe (WHO/Europe), 74% of the European Union’s 27 member states use AI diagnostic tools, including those for medical imaging and disease detection. WHO identified workforce training, data governance and common standards for safe deployment as key priorities. Clinical use requires an operational framework in which healthcare professionals understand the technology’s limitations and potential errors and remain accountable for final decisions.
The European Health Data Space (EHDS) includes medical images and related reports among the priority categories of health data for exchange. Interoperability and access-logging requirements for electronic health record systems are scheduled to take effect in phases from 2029 and 2031, depending on the data category. EHDS establishes a framework for secure data exchange between healthcare institutions and countries, distinct from defining medical imaging technical standards themselves. Data portability when institutions replace systems or add another company’s AI can reduce supplier dependence and duplicative integration costs.
Deep integration with hospital systems can strengthen contract retention while creating a risk of vendor lock-in. The ability to transfer data and previous analysis results to another product is important. So is the ability to add external algorithms. Long-term competitiveness should be assessed through a company’s ability to secure renewals on the strength of standardized integration and demonstrated clinical outcomes.
Coreline Soft is a representative example of this shift in medical imaging AI. Its expanding German contracts, U.S. subscription relationship and overseas revenue growth indicate that the sector is moving beyond regulatory authorization and initial deployment toward sustained use. The next stage of assessment is to examine the proportion of contracted institutions actively using the product and how examination volumes translate into insurance claims, revenue recognition, contract renewal and cash collection. Assessing recurring revenue profitability also requires accounting for cloud infrastructure, system integration, security, clinical staff training and customer support costs. Alongside contract scale, the long-term value of medical AI companies depends on whether products embedded in clinical practice can generate sustained cash flow and improve patient outcomes.
