Alex Yang, an 18-year-old high school senior in Seoul, has founded Reteena, an AI research and development startup focused on improving Alzheimer’s diagnostics. What makes this story remarkable is that Yang built the entire company with partners he found online and has never met in person, spanning locations from California to Florida to Michigan.
Motivated by family stories of relatives battling Alzheimer’s disease, Yang entered a health accessibility competition during high school. Recognizing he couldn’t tackle the challenge alone, he spent a month recruiting partners through internet forums, Discord servers, and GitHub repositories. After numerous rejections, he assembled a team of six high schoolers who believed in his vision to revolutionize Alzheimer’s diagnostics.
The startup’s initial research focused on enhancing low-field MRI image resolution using machine learning and deep learning techniques. Low-field MRI machines are portable and more economically accessible than traditional MRI equipment, making them ideal for underserved communities. Reteena’s goal was to make these more affordable diagnostic tools reliable enough for widespread Alzheimer’s detection.
Despite managing across multiple time zones from his Seoul base (starting work at 3 a.m. to coordinate with U.S.-based team members), Yang developed a “follow-the-sun” workflow using shared Notion workspaces. The team’s dedication paid off when their first research paper was published at the IEEE BigData 2024 Conference, a prestigious artificial intelligence research venue.
The startup has since expanded to 12 members and conducted additional research on how speech pattern changes and genetic markers could enable earlier Alzheimer’s detection. By transparently sharing their journey on LinkedIn, they attracted mentorship from Y Combinator founders and Pear VC partners, gaining credibility despite their young age.
Most recently, Reteena launched Remembrance, their first consumer product—an AI therapeutic service designed to help Alzheimer’s patients reconnect with memories. The system uses gentle questioning to trigger old memories through reminiscence therapy, automatically organizing recalled memories in knowledge graph databases to create a personalized memory archive that patients can revisit.
The team has faced significant challenges, particularly when attempting to test their product with actual patient data at local hospitals, where they encountered compliance requirements and institutional review board obstacles. However, Yang views these early-stage failures as valuable learning experiences that are better encountered at 17-18 than later in life when stakes feel higher.
Key Quotes
We applied machine learning and deep learning techniques to enhance the quality of low-field MRI scans. Our goal was radical — to make Alzheimer’s diagnostics accessible and affordable for underserved communities by making low-field MRI more reliable.
Yang explains Reteena’s core technical approach and mission. This quote is significant because it demonstrates how the team is using AI to address both a technical challenge (image quality enhancement) and a social equity issue (healthcare accessibility for underserved populations).
When our first piece of research was published in the IEEE BigData 2024 Conference, a prestigious annual conference for artificial intelligence research, I felt exhilarated. It was proof that we were onto something and justification for every late night.
Yang reflects on achieving academic validation for their AI research. This matters because it shows that despite being high schoolers working remotely, the team produced research rigorous enough for acceptance at a major AI conference, lending credibility to their work.
Most recently, we launched our first consumer product, Remembrance, an AI therapeutic service to try to help Alzheimer’s patients reconnect with their past. It works by asking gentle questions designed to trigger old memories through reminiscence therapy.
Yang describes their AI-powered therapeutic product. This quote is important because it shows the team’s evolution from research to practical application, demonstrating how AI can be deployed not just for diagnosis but for ongoing patient care and quality of life improvement.
Our Take
Reteena’s story exemplifies how AI development has become increasingly accessible to motivated individuals regardless of age or geographic location. The fact that high schoolers can conduct publishable AI research and launch functional products signals a fundamental shift in the technology landscape. However, Yang’s candid discussion of obstacles—particularly regulatory barriers in healthcare—provides important context about the gap between building AI solutions and deploying them in regulated industries. The use of knowledge graphs for memory organization in Remembrance is particularly sophisticated, suggesting the team has genuine technical depth. While the startup’s ultimate success remains uncertain, the journey demonstrates how AI tools are empowering a new generation to tackle complex problems like Alzheimer’s diagnostics and care, potentially accelerating innovation in healthcare AI through fresh perspectives unconstrained by traditional industry assumptions.
Why This Matters
This story represents a significant trend in democratizing AI development and healthcare innovation. Yang’s success in building a legitimate AI research startup while still in high school—complete with IEEE conference publications and a launched product—demonstrates how accessible AI tools and online collaboration platforms have lowered barriers to entry in the technology sector.
The application of machine learning to medical diagnostics, particularly for Alzheimer’s disease, addresses a critical healthcare challenge. With Alzheimer’s affecting millions globally and early detection being crucial for treatment efficacy, AI-enhanced diagnostic tools could transform patient outcomes. Reteena’s focus on making diagnostics accessible and affordable through low-field MRI enhancement targets a genuine market gap.
The story also highlights how AI therapeutic applications like Remembrance are expanding beyond diagnosis into treatment support. Using AI for reminiscence therapy and memory organization represents an innovative approach to patient care that could scale more affordably than traditional therapeutic interventions. This signals a broader trend of AI moving from purely diagnostic applications into therapeutic and care management roles in healthcare.
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Source: https://www.businessinsider.com/how-high-schooler-launched-startup-with-people-he-met-online-2025-12