Artificial intelligence is becoming one of the most heavily promoted technologies in medical diagnostics. AI-powered biosensors, automatic interpretation systems, and machine learning-assisted point-of-care platforms are now appearing in hospitals, startups, and decentralized healthcare models around the world. But many diagnostic experts argue that behind the excitement, the industry still faces quiet problems that AI alone cannot solve: unstable measurement systems and unreliable real-world data.
AI diagnostics is growing faster than the systems beneath it
Artificial intelligence is increasingly integrated into medical devices, imaging systems, biosensors, and point-of-care diagnostic platforms. of US Food and Drug Administration (FDA) currently has a growing list of AI-enabled medical devices that have completed the regulatory review process for safety and effectiveness.
At the same time, regulators are also recognizing that AI medical systems pose new operational risks after implementation. In January 2025, FDA asked for public feedback on: How should AI-enabled medical devices be evaluated in actual clinical settings?This includes performance drift, workflow changes, user interaction, and reliability issues over time.
Hyoarm JeongAs the debate continues regarding the widening gap between AI hype and real-world diagnostic reliability, CTO and co-founder of Kompass Diagnostics told KoreaTechDesk:
“AI only has real value when the underlying measurement systems produce highly structured, consistent, and correlated data.”
Joung’s experience includes AI-driven biosensor research at UCLA, including point-of-care diagnostic platforms, multiplex biomarker detection systems, and deep learning-assisted sensing technologies. His perspective reflects growing concerns within the deep technology commercialization of healthcare. AI performance is highly dependent on the stability of the physical diagnostic systems that generate the data.
AI’s biggest problems in diagnostics may start before the algorithms
Discussions of healthcare AI often focus on model architecture, computational performance, or generative AI capabilities. but, Diagnostic systems behave differently Because the underlying data is physically generated through the operation of biological samples, fluidics, chemistry, optics, electronics, and sensors, it can be obtained from many purely digital software environments.
“The foundation for successful AI integration is the quality and control of the training data, not the model itself.”
John explained.
He said the following happens in real-world diagnostic systems: Handling many uncontrolled variables simultaneouslyThis includes sample variations, environmental conditions, manufacturing variations, sensor instability, and differences in user handling.
“If these factors are not well controlled, the resulting dataset can contain large amounts of variability and outliers, significantly reducing the robustness and generalizability of the model.”
The FDA similarly acknowledges that AI-enabled medical devices pose unique regulatory challenges as many systems evolve through data-driven learning processes. Regulators are increasingly examining how AI systems perform after deployment under changing real-world conditions, rather than evaluating performance only in the early stages of development.
This issue has become especially important as healthcare providers advance diagnostics. distributed environment It becomes difficult to maintain consistency in environments and operations, whether in a doctor’s office, pharmacy, or home.
Why AI can’t automatically save vulnerable diagnostic platforms
The rapid growth of healthcare AI has led to a common assumption that algorithms can compensate for weaknesses in sensing systems and diagnostic workflows. John often believes that assumption. Overestimating what AI can realistically accomplish.
“AI creates measurable value when used on a mature, systemically controlled diagnostic platform.”
he said.
In these situations, AI can help improve signal interpretation, identify subtle analytical patterns, automate quantification, and compensate for small fluctuations within an already stable system.
Research published in nature communications This year, we discussed how machine learning is being incorporated into point-of-care testing systems such as lateral flow assays, nucleic acid amplification tests, and image-based diagnostics. However, the researchers also point out that: Reliability, regulatory validation, and real-world integration remain major barriers Toward large-scale clinical implementation.
Joung believes the problem becomes even more difficult when the underlying sensing platform itself is lacking. reproducibility.
“If the underlying sensing systems themselves are not yet sufficiently standardized or reproducible, AI may introduce additional complexity without meaningfully improving real-world results.”
That additional complexity can appear simultaneously across multiple layers, including validation requirements, regulatory oversight, post-market monitoring, model retraining, and quality control controls.
During the health check, Inaccurate or unstable measurements It’s not just a technical inconvenience. they can Directly influences medical interpretation and patient decision-making.
AI diagnostics is increasingly becoming a regulatory infrastructure issue
The growing role of AI in healthcare is also reshaping regulatory systems globally.
In 2025, International Medical Device Regulators Forum (IMDRF); To that member F.D.A. and Korean Ministry of Food and Drug Safety (MFDS), completed updated Good machine learning practices Principles of medical device development. Frameworks highlighted Lifecycle monitoring, data quality, validation control, system reliability throughout the introduction.
South Korea also continues to expand its own AI healthcare governance framework. In May 2025, MFDS Updated guidance on approval and clinical trial design for AI-applied digital medical devices. Earlier this year, the ministry further issued a statement explaining: The world’s first approval review guidelines in particular Targeting generative AI medical devices.
However, despite accelerating regulatory activity, researchers continue to warn that AI health systems still face challenges. unresolved issues get involved Reliability, explainability, clinical validation, and real-world deployment.
2025 editorial published in Korean Journal of Radiology Although South Korea’s guidance framework represents meaningful progress, it noted that broader generalist medical AI systems may still exceed the limitations of traditional medical device evaluation structures.
For healthcare startups, this can be a difficult balancing act. AI innovation is advancing rapidly, but healthcare deployment remains dependent on: Reliable, repeatable and controllable performance Under clinical conditions.
The next competitive advantage may depend on better data, not bigger models
The healthcare industry is unlikely to ease its investment in AI diagnostics. AI-assisted biosensors, distributed testing systems, and automated clinical analysis platforms continue to attract intense attention from startups, investors, and regulators around the world.
However, Joung believes that long-term success is more important than adding AI to an unstable system. Build a mature diagnostic infrastructure that can consistently produce reliable data.
“I see successful AI integration as evolving with automation, manufacturing maturity, and system-level process control, rather than as a standalone solution applied on top of unstable systems.”
This perspective is likely to become increasingly important as healthcare AI moves beyond initial demonstrations to large-scale clinical deployment.
The toughest issue in AI diagnostics may still be reliability
The diagnostics industry has already demonstrated that AI can aid interpretation, automate analysis, and improve the detection of certain forms of signals. However, the healthcare sector may now be entering a more difficult phase. Long-term reliability is more important than technological novelty Alone.
For startups, investors, and health systems, the next challenge may be more than just building smarter algorithms. may be related Build a diagnostic platform stable enough to trust the data generated by AI.
As AI diagnostics continues to expand globally, the systems that generate measurements may ultimately become as important as the models that interpret them.
Important points
- AI diagnostics is highly dependent on the quality and stability of the underlying sensing system.It’s not just about algorithm performance.
- According to Hyoarm John, AI is only valuable if the diagnostic platform produces data that is structured, consistent, and controllable.
- A real diagnostic environment introduces uncontrollable variables such as: Sample variations, environmental conditions, manufacturing variations, sensor instability, etc.
- The FDA and international regulators are increasingly focused on: Real-world AI medical device reliability, lifecycle monitoring, and post-deployment performance drift.
- Research in point-of-care diagnostics continues to show that: AI-assisted biosensors still face major challenges such as validation, reproducibility, and clinical integration.
- As medical AI governance becomes increasingly complex globally, South Korea’s MFDS has expanded its AI medical device guidance and clinical trial framework.
- Jung argues that: Rather than serving as a standalone remediation layer for unstable systems, AI integration must evolve with automation, manufacturing maturity, and system-level process control.
🤝 Do you want to connect with established Korean companies operating all over the world?
Browse carefully selected company profiles and request a direct introduction. beSUCCESS connect.
– Stay one step ahead of the Korean startup scene –
Get real-time insights, funding updates, and policy changes shaping South Korea’s innovation ecosystem.
➡️ Follow KoreaTechDesk LinkedIn, X (Twitter), Threads, Bluesky, Telegram, Facebookand WhatsApp channel.
