Subtitle: From Observation-based AI to Action-oriented 'Agents'
Having spent 26 years at Samsung Electronics and 12 years as a National R&D Evaluator, I have stood at the forefront of South Korea's technological evolution. Every year, massive national budgets are deployed, and AI models with world-shaking performance pour out. However, today, I wish to open the first report of AEGE-LAB with the word 'Regret' rather than 'Success.' Despite the triumphs on paper, the lives on the front lines—those we are meant to save—remain alienated from the benefits of technology.
[Image representing the reality of the hospital ward vs. lab-bound technology]
1. The Trap of Metrics: Patients Unsaved by 95% Accuracy
National R&D evaluation sites are festivals of brilliant numbers: 95.3% accuracy, 12 SCI-level papers, and dozens of patent filings. These figures suggest South Korean medical AI is at the global zenith. Yet, from my seat as an evaluator, I always pose a cold question: "Does this solution contribute even one minute to caring for a patient in a ward tonight?"
The reality is harsh. Algorithms boasting superior technology are 'perfect' only within high-performance servers in laboratories. Even at this moment, no AI agent exists beside the elderly woman struggling for breath in Room 305. The failure of technology to cross the 'Last Mile' into the clinical field, where life is at stake, is the most painful truth of our AI industry.
2. The Limits of 'Presentation Tech' and the High Walls of Governance
The ironclad rule I learned on the front lines of B2B marketing at Samsung is simple: "Technology that is not used by the customer has no value." Yet, the current medical AI R&D ecosystem is buried in 'Presentation Performance'—the quantity of papers and quantitative metrics—rather than real-world clinical value.
Even when innovative solutions are developed, they are blocked not by a lack of technology, but by the rigid 'Walls of Institutions.' What is the use of increasing GPU infrastructure by 50,000 or 100,000 units if the 'Medical Reimbursement' system for adopting AI in hospitals is blocked by bureaucratic silos? An evaluation system that judges success by the number of papers rather than market share or patient survival rates will only mass-produce AI that sleeps forever in the lab.
3. The AEGE-LAB Mission: From Observing AI to Acting 'Agents'
The massive infrastructure of 50,000 GPUs must not be wasted as mere heat in data centers. Our goal is clear: to ensure the grandmother in Room 305 can sleep peacefully, AI must move beyond being a data analysis tool to become an 'Agent' that proactively judges and acts on the ground.
To achieve this, 'People' and 'Understanding' must come before technology. Stakeholders—government officials, doctors, and nurses—must define what Agentic AI is and why it can transform medical workflows. We must boldly discard formal paper-centric metrics and shift our Key Performance Indicators (KPIs) to 'Actual Patient Application Cases.' Building an integrated governance system where technology supply and medical fee rewards work in harmony—moving toward 'Execution-type AI'—is the essence and mission of AEGE-LAB.
"For more in-depth analysis, please visit our [Medical AI Report Center]."
✍️ Dr. Paul Kim (Hang-seop Kim), Ph.D.
- Director of AEGE-LAB / Ph.D. in Information Systems, Yonsei University
- Formerly: 26 years at Samsung Electronics & Samsung Human Resources Development Center
- Currently: National R&D Evaluation Member (12th Year)
- Currently: Professor, Graduate School of Health & Medical Management, CHA University
[Request for Expert Insights]
I look forward to your sharp insights regarding the medical AI R&D innovation and the integration of inter-ministerial governance proposed in this report. Your valuable opinions will be reflected in AEGE-LAB’s future policy proposals and as core arguments in my upcoming books.
(Many are already sharing deep reviews and insights through my books, 2027 Robot War and A Healthy Journey with an AI Doctor. I invite you to add your voice to this intellectual solidarity.)