How MAEA Solves the Data Lifecycle Problem
Sizing a medical AI dataset is arithmetic. Keeping that dataset provable, versioned, and reusable across years of development is the part that fails, and it is what MAEA is built to hold together.
This U.S. immersion will accelerate partnerships, pilots, and MAEA’s rollout to advance medical AI.

By ModAstera
15 Aug 2025
We’re excited to share that ModAstera has been selected for the FY2025 Beyond JAPAN On-Site Program, Deep Tech (Bio & Health Tech) Course in San Diego. The official announcement lists ModAstera among the Deep Tech cohort alongside leading life-science innovators. (B4D JAPAN)
Beyond JAPAN (a JETRO/J-StarX initiative) offers a local, on-site immersion designed for founders and CXOs to build traction in the U.S. market. The Deep Tech Course focuses on Life Sciences in San Diego, the West Coast’s largest life-science cluster—home to world-class research institutes, medical device companies, and a thriving “academia–industry–government” ecosystem. (B4D JAPAN)
Participation in the San Diego track directly supports our mission to accelerate medical AI development and bring MAEA—the Medical AI Engineering Agent—to teams that need faster, compliant AI pipelines. While on site, we plan to: Meet clinicians, researchers, and medtech builders to explore clinical pilots and co-development opportunities. Engage with local accelerators, investors, and enterprise partners to scale no-code/low-code medical AI solutions. Validate U.S. market needs around AI-assisted annotation, rapid prototyping, and fast time-to-deploy for SaMD workflows. We’re grateful to the organizers and partners of Beyond JAPAN for the opportunity, and we look forward to contributing to San Diego’s vibrant life-science community. (B4D JAPAN)
We’ll share updates from San Diego; key learnings, partnerships, and product announcements, as we work to make medical innovation move at the speed of thought. If you’d like to connect while we’re in town, please reach out through our website
Beyond JAPAN FY2025 selection news; https://lnkd.in/giq7AYje
Sizing a medical AI dataset is arithmetic. Keeping that dataset provable, versioned, and reusable across years of development is the part that fails, and it is what MAEA is built to hold together.
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