This article serves as a preliminary study in Noah Medical's series of in-depth reports, aiming to help investors establish a core value judgment framework for the medical robotics industry.

As the medical robotics industry continues to heat up, the capital market's understanding of the sector is quietly changing.

In the past few years, the market has focused more on technical parameters, product performance, and commercialization pace. However, as the industry has matured, institutional investors have begun to return to a more fundamental question: whether companies have entered into core segments that truly have clinical value.

This line of reasoning is redefining the industry's valuation system.

In the field of lung cancer, which has a high incidence and mortality rate, a key aspect that has long been neglected is gradually coming into the view of capital: as low-dose CT screening (LDCT) continues to become more widespread, "detecting problems" is no longer a problem, but "how to make an accurate diagnosis" is becoming the core variable affecting the efficiency and outcome of diagnosis and treatment.

Behind the widespread adoption of early screening: Diagnostic capabilities become a new investment focus

In recent years, with the widespread use of low-dose spiral CT in routine physical examinations, the detection rate of pulmonary nodules has continued to increase, and a large number of pulmonary nodules have been identified at an early stage when they are asymptomatic, reflecting that chest imaging examinations are evolving from auxiliary diagnostic tools to front-end, routine screening methods.

However, moving the detection process earlier has not entirely translated into improved diagnostic and treatment efficiency. On the one hand, because the lungs themselves lack pain receptors, the growth of early-stage lung cancer lesions often does not cause obvious symptoms in patients. This directly leads to patients being unable to actively detect the disease, and clinically, a large proportion of patients are diagnosed at an advanced stage, resulting in a 5-year survival rate of less than 20%. On the other hand, the accuracy and safety of existing pathological diagnostic methods, such as tissue biopsy, are still affected by factors such as the location and morphology of the lesion and the operator's experience. Furthermore, different biopsy methods pose significantly different clinical risks to patients. Overall, the diagnostic process remains one of the key constraints in the current treatment pathway.

In other words, "finding more nodules" has not simultaneously led to "higher quality diagnoses." This structural mismatch has transformed the diagnostic process from a supporting step into a crucial yet uncertain part of the entire lung cancer treatment system. Only by improving the early diagnosis rate can early treatment be achieved, integrating diagnosis and treatment. For the capital market, these areas with definite demand but insufficient capacity often signify the formation of new value.

Peripheral pulmonary nodules: A core challenge in the medical robotics field

Among all pulmonary nodule diagnosis scenarios, small peripheral pulmonary nodules are generally considered to be the most difficult to diagnose.

On the one hand, the lung structure is tree-like and branched, with the path becoming finer and more complex towards the periphery, which limits the reach of traditional bronchoscopy. On the other hand, preoperative CT images are static information, while patients are in continuous respiratory motion during surgery, and the deviation between the images and the actual anatomy is common, namely "CT-to-body divergence".

This means that even when the biopsy tool is "close to the target," doctors still need to rely on experience to determine whether they have actually entered the lesion. In this process, the diagnostic results are highly dependent on individual experience, and their stability and reproducibility are uncertain. Essentially, this is not an efficiency issue, but a capability issue—that is, in complex structures and dynamic environments, a standardized and reproducible diagnostic capability has not yet been developed.

For this reason, early diagnosis and treatment of peripheral pulmonary nodules are considered by the industry as one of the few key scenarios in the medical robotics field with "capability barriers." Once a breakthrough is achieved, its value will not only be reflected in the improvement of single-point performance, but may also have a restructuring effect on the entire diagnosis and treatment pathway.

Noah Medical: Targeting the Diagnostic Breakpoint and Reconstructing the Integrated Diagnosis and Treatment Pathway for Lung Cancer

Against this backdrop, Noah Medical's technological approach has gradually attracted the attention of investors.

The company did not stay on the traditional "navigation-assisted" technical approach, but instead directly tackled the highly challenging scenario of real-time location and diagnosis of small peripheral lung nodules, and systematically developed a strategy around the two core capabilities of "reaching the lesion" and "confirming the location".

The company's core product, the Galaxy platform, integrates imaging systems, AI algorithms, and robotics. Through embedded imaging capabilities, it updates lesion locations in real time during surgery, effectively avoiding discrepancies between CT images and actual anatomy. Simultaneously, the flexible endoscope and tool design based on natural cavities allows the device to access the distal lung regions, achieving more stable access in complex structures.

This combination of "reaching and locating" capability is the core and also the most difficult aspect to overcome in the current diagnosis of peripheral pulmonary nodules.

Building on this foundation, the Galaxy platform further extends to "integrated diagnosis and treatment," which means completing biopsies and treatments within a single process, driving a shift in the diagnosis and treatment model from "step-by-step execution" to "integrated diagnosis and treatment." This model not only helps improve efficiency but, more importantly, reduces the complexity and uncertainty caused by multiple surgeries for the same patient, enabling the diagnosis of lung nodules and early treatment of lung cancer through a single procedure.

Clinical data suggests that this approach has a solid foundation for validation. The Galaxy system has completed over 10,000 surgeries, achieving a diagnostic rate of over 90% in relevant studies, with some studies approaching 96.7%, while also demonstrating stable safety.

For investors, the significance of these data lies not only in the performance improvement itself, but also in the trend they reflect—in the diagnostic process, which has long relied on experience, system capabilities are gradually replacing individual judgment, and the diagnostic process is beginning to have the possibility of standardized replication.

From single-point breakthroughs to platform opportunities, why is capital re-examining this sector?

From a longer-term perspective, Noah Medical's value is not limited to a single product or a single scenario.

The technological system built around real-time imaging, high-precision positioning, and flexible access has the potential to be extended to natural cavity scenarios such as the digestive tract and urology. Once the "high-precision operation capability in complex environments" is verified, its potential for cross-departmental replication will be significantly unlocked.

At the same time, a platform-based approach also means a more scalable business model. Beyond equipment, disposable consumables, software systems, and service capabilities are all expected to generate sustainable revenue streams, thus building a more stable profit structure. From a capital perspective, this logic is similar to the evolutionary path of the early surgical robot sector—from single-point technological breakthroughs to a long-term sector with platform attributes.

Conclusion

Noah Medical has not merely focused on comparing traditional robotic technical parameters. Instead, it has proposed a clinically valuable solution addressing a real clinical pain point. By integrating imaging, AI algorithms, and robotic systems, it attempts to transform the previously experience-dependent diagnostic process into a standardized, system-wide capability. Based on current commercial clinical data and technological pathways, this direction has begun to receive initial validation.

For institutional investors, these targets in the "early stages of capability building" often possess both technological barriers and potential for long-term expansion. As the industry gradually returns to its essence of "problem-solving," companies that can establish definitive capabilities in key clinical stages are expected to stand out in the next round of value reassessment.

— This is the second in a series of in-depth studies by Noah Medical —

This article was originally published in the Hong Kong Economic Journal and is reprinted with permission from Tianjin Puli Ark Medical Technology Co., Ltd. Copyright belongs to the original author. Please contact the original source for reprint permission. This article does not constitute medical advice; please consult your doctor for specific diagnosis and treatment.