myTomorrows is a health tech scale-up that connects patients with potentially life-saving treatments that are not yet approved for public use.Doctors submit de-identified medical profiles, and AI matches them to relevant clinical trials and expanded access programs.The platform deploys large language models in Europe on AWS to meet GDPR requirements and operate at scale.
Use case type
Patient matching
Patient matching groups 2 documented AI deployments in the AI Use Case Hub. Adoption so far is concentrated in Healthcare, led by Netherlands. Teams most often build it with AWS and Large Language Models. Browse the company examples below to see how teams put it into production.
Use cases
2
Examples
2
Industries
1
Timeline
2 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
1 case documented across 4 months (Mar 26 – Jun 26), peaking at 1 in March 2026.
1 so far in July 2026 (in progress, not charted)
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
Company examples
Use cases of this type
2 shown from 2 use cases
MicrosoftMar 4, 2026
AI-enabled Enterprise Master Patient Index (EMPI) using Azure AI Foundry agent, Cosmos DB graph, and LLM-assisted matching
Healthcare organizations often struggle with duplicate and incorrect patient records across multiple EHRs and source systems, which drives large manual review queues in EMPI matching.The article describes a custom EMPI built on Azure that combines deterministic matching, probabilistic scoring, and AI-enhanced semantic similarity to improve patient identity resolution.Patient identifiers, addresses, and relationship edges are stored in Azure Cosmos DB Gremlin graph, while an Azure AI Foundry agent in a Streamlit dashboard lets data stewards search, compare, approve, and reject candidate matches.
Common questions
Patient matching at a glance
- How many patient matching use cases are documented?
- The AI Use Case Hub documents 2 real patient matching deployments across 1 industries, with 2 detailed company examples you can browse.
- Which industries adopt patient matching the most?
- Patient matching is most common in Healthcare (100%).
- Which countries lead in patient matching?
- Netherlands leads documented patient matching deployments, followed by United States.
- What technologies are used for patient matching?
- Teams most often build patient matching with AWS, Large Language Models and Azure AI Foundry Agent Service.
- What AI capabilities power patient matching?
- Across the documented deployments, the most common capability patterns are Agent (100%).
- What results do companies report from patient matching?
- Across the 2 deployments reporting outcomes, companies most often cite customer experience & trust (50%), market & geographic expansion (50%) and other strategic outcome (50%).