Tier-3 hospital multimodal EHR copilot
Imaging, labs and EHR text fused for one-shot reasoning, structured care recommendations auto-generated.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Project Overview
- Customer type
- Top-tier Tier-3 hospitals & healthcare groups
- Project duration
- 6–8 weeks
- Tech stack
- Multimodal backbone · Multi-agent
- Deployment
- Private deployment
2. Business Context & Pain Points
The customer is a Enterprise / Group player in Healthcare, with the core pain concentrated in "Knowledge & Q&A". Manual workflows and point tools could not scale or stay consistent, driving up response time, operating cost and compliance risk. This project delivered an end-to-end AI upgrade for that workflow.
3. Solution
Anchored on "Native Multimodal Unified Foundation", Global AI DAO integrated the following capability stack into a single solution:
- Multimodal backbone —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Multi-agent —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Private deployment, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
Source metric: 5× chart-prep productivity, <1% key-metric miss rate.
5. Specs & Fit
| Architecture | Hybrid AR-text + diffusion image/video |
| Modalities | Text / code / audio / image / video |
| Context window | 128K–1M (tiered) |
| Train/infer precision | BF16 / FP8 / INT8 |
| Chip compatibility | NVIDIA / Ascend / Cambricon |
| Deployment | Private / hybrid cloud / edge |