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The AI development platform built for medical teams.

Annotate, train, validate, and deploy medical AI in one workflow, with healthcare-ready compliance support built in.

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Trusted across the medical AI ecosystem

Programs, research groups, and healthcare collaborators use ModAstera to move faster with less platform complexity.

The Problem

Despite the potential of AI to transform healthcare, companies still waste time and money to utilize data for risk prediction and patients’ outcome improvement.

High Cost & Long Timelines

AI projects are expensive and slow by default.

From scoping the problem to hiring engineers and maintaining infrastructure, the time and money required across dozens of steps add up fast.

Typical project cost: $150,000–$500,000

Typical timeline: 6 months to several years for custom medical AI via traditional routes

Impact: budget overruns, stalled pilots, and delayed patient benefits

Tools Too Complex for Domain Experts

Clinicians have the insights—but current tools demand skills they weren’t trained to use.

Most large-firm solutions are powerful yet not streamlined for custom use, forcing teams to depend on scarce ML talent and lengthy hand-offs.

Steep learning curves for non-technical users
Fragmented toolchains that slow collaboration

Impact: ideas stay on the whiteboard; innovation cycles drag on

Data Preparation: The Biggest Bottleneck

Poor data → poor models.

Medical AI needs precise annotation and segmentation—work that’s time-consuming, slow, and error-prone when done manually.

Extensive labeling requirements across images, signals, and records

Quality assurance adds more time and rework

Impact: inconsistent datasets, weaker model performance, and longer R&D cycles

One workflow from raw data to production-ready AI

ModAstera brings the critical stages of medical AI delivery into a single operating surface so teams stop losing time to handoffs and disconnected tools.

01 Prepare better data

Annotate faster with AI assistance

Turn raw studies into training-ready datasets with assisted labeling, review workflows, and lineage your team can trust.

See the platform

Use Cases

How medical teams use ModAstera in practice

Each story shows the bottleneck, what changed inside the workflow, and the operational outcome.

Deployment dashboard for a medical AI application — Validated model to production endpoint without a months-long DevOps project.

Use Case 01

Deploy a medical AI application without a long platform build

Bottleneck: Teams can validate a model but still wait months on infrastructure, security review, and API delivery before clinicians or partners can use it.
What changed: ModAstera packages validation, environment controls, and deployment into one workflow so teams move from approved model to a secure endpoint without rebuilding the stack.
Outcome: A mobile AI-assisted diagnosis app launched in 30 days instead of stretching into a seven-month deployment cycle.

Training workspace for rapid medical AI experimentation — Researchers iterate in one workspace instead of stitching together setup, compute, and tracking.

Use Case 02

Prototype research models while the study is still moving

Bottleneck: Research groups lose grant time to pipeline setup, experiment tracking, and compute hand-offs before they can test a serious idea.
What changed: MAEA lets teams define experiments, compare versions, and manage compute in one place so model exploration happens while the research question is still fresh.
Outcome: A Japanese university team cut an MRI brain-age prototype from a 90-day window to less than a week.

AI-assisted annotation workspace for medical data preparation — Experts review draft labels instead of starting every case from scratch.

Use Case 03

Prepare labeled data without drowning experts in manual review

Bottleneck: High-quality labels are still the biggest delay in medical AI, especially when specialists have to annotate every sample manually.
What changed: AI-assisted pre-labeling, review workflows, and traceable edits let specialists focus on corrections and edge cases instead of repetitive first-pass annotation.
Outcome: A surgery data company reduced annotation time per slide from hours to minutes.

Workflow dashboard for regulatory screening automation — Document triage and review signals surfaced before manual queues pile up.

Use Case 04

Automate regulatory screening before queues become backlogs

Bottleneck: Regulatory screening teams can be overwhelmed by document queues, repetitive checks, and uneven review quality.
What changed: Workflow automation screens incoming documents, flags gaps early, and gives staff a more consistent starting point for advisory review.
Outcome: A backlog that had stretched for months was cleared in days, with stronger submissions and fewer avoidable rejections.

What Our Partners Say

Hear from leading healthcare organizations and researchers who are transforming medical AI development with ModAstera.

“

PulSec Inc.

With the support of ModAstera, we have trained a model to learn tongue diagnosis from Traditional Chinese Medicine doctors and classify features from tongue images. By leveraging ModAstera's MAEA, we can build AI models without writing code. For startups like ours, which operate with limited resources, this enables us to reduce development costs and accelerate AI development—making it an especially attractive solution.

Shoji Maruyama

CEO, PulSec Inc.

Surg Storage

ModAstera is tackling one of the most critical challenges in healthcare today—making medical AI truly accessible and scalable. Their approach is not only visionary but also grounded in practical execution. As a medical data company, Surg storage Co.,Ltd. is proud to support such forward-thinking innovators. We look forward to contributing to ModAstera's journey as they continue shaping the future of medical AI.

Akihiro Hirao

CEO, Surg Storage

Surg Storage

Tohoku University Hospital

I have collaborated with the team of ModAstera regarding a development of convolutional neuronal network (CNN) models to predict aging and lifestyle/cardiovascular diseases from medical image phenotypes. The work of ModAstera was fast and has provided CNN models with high predictability. I very much appreciate the contribution of ModAstera with the great supports on our project.

Assistant Professor

Tohoku University Hospital

Products

ModAstera is centered on full-lifecycle medical AI development, with Hebra extending the portfolio into patient-facing dermatology triage.

MAEA

End-to-end medical AI development, from data preparation to deployment.

Explore MAEA

Hebra

AI skin condition triage and fast connection to dermatologists.

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Choose Your Plan

Select the perfect plan for your healthcare AI development needs

Starter

Best for personal evaluation and early exploration

Ideal for personal evaluation and early exploration

Manage datasets and annotate with AI-assist

Train 2 predictive models

Storage up to 100 MB

Deploy 1 model with capped inference compute in the platform interface

Get Started

Team

Perfect for researchers and small teams

Ideal for researchers and small teams, up to 3 users

Manage datasets and annotate with unlimited AI-assist

Train unlimited predictive models

Storage up to 10 GB

Deploy models in one-click with API access

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Popular

Organization

Perfect for growing teams

Everything in Team

Collaborative workspace with teammates

Train GPU-based models

Organization level administration

Enterprise

For large-scale deployments

Custom setup

Unlimited everything

Dedicated support

Custom integrations

SLA guarantees

See where your medical AI workflow is slowing down.

Book a live walkthrough to see how ModAstera can shorten the path from dataset preparation to deployment.

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