Case Study: How Elxo Deployed High-Accuracy Clinical AI Safely and at Speed with Conode

The Challenge: Extracting Clinical Insights Without Sacrificing Accuracy, Speed, or Safety. Elxo Inc., a healthcare organization focused on improving clinical intelligence, faced a major challenge: extracting reliable, structured diagnostic insights from large volumes of unstructured doctors’ notes.

They encountered several persistent hurdles:

• Low Accuracy: Traditional NLP tools delivered less than 80% accuracy—missing critical diagnostic cues and introducing clinical risk.

• High Costs: Tuning and managing models manually demanded extensive time and resources.

• Data Inconsistency: Each new dataset required rebuilding models from scratch—leading to time delays and inefficiencies.

Elxo needed a way to deploy AI-powered feature extraction quickly—but without sacrificing reliability, traceability, or safety.

Key Results with Conode

Using Conode, Elxo extracted clinical features from unstructured text with >95% accuracy in just a few development hours. This translated into:

• Cost Reduction by minimizing the need for human review and manual tuning

• Improved Reliability with fewer errors in interpreting patient records

• Greater Scalability—new datasets could be integrated without rebuilding models

• Safer AI Deployment, with full visibility into model decisions and failure modes

• No-Code Setup, making the entire process accessible to clinical and data teams alike

The results were nothing short of transformative. Let’s take a look at how we tackled this project with Conode...

Step 1: Automatic Knowledge Graph Creation

With a single click, Elxo uploaded their clinical note data into Conode. The platform instantly generated a unified knowledge graph, converting unstructured medical text into a structured, interpretable format.

This knowledge graph became the foundation for a safe and transparent AI workflow—enabling full traceability and auditability throughout the pipeline.

Step 2: Medical Feature Extraction via Natural Language

Armed with Conode’s Extract AI Agent, Elxo extracted key diagnostic information simply by asking natural language questions. Results were:

• Paired with confidence scores

• Linked directly to source records

• Reviewed quickly for failure modes and edge cases.

This observability helped the team spot risky outputs early—before deployment—and take corrective action with minimal effort.

Step 3: Monitoring Model Behavior Across Embedding Space

Using Conode, Elxo mapped clinical notes across a high-dimensional embedding space. This allowed them to:

• Instantly locate underperforming clusters

• Identify hidden failure modes

• Make targeted improvements 

Here we plot the clinical notes in an embedding to quickly identify areas of poor accuracy and address them efficiently. Repeating this process resulted in improved model performance within about an hour’s work.

Step 4: Few-Shot Learning for Safe and Fast Iteration

Instead of retraining large models from scratch, Elxo used few-shot learning within Conode to fine-tune performance using just a handful of examples. This approach:

• Reduced development time

• Prevented unintended regressions

• Improved model behavior safely across datasets.

The Impact: Safer, Smarter AI—Deployed in Hours, Not Months.

With Conode, Elxo achieved clinical-grade accuracy at unprecedented speed—deploying an AI-powered feature extraction pipeline that was:

• >95% accurate

• Fully transparent and auditable

• No-code and scalable across teams and datasets

Why This Matters

AI in healthcare must be accurate, explainable, and safe. Elxo’s success shows how Conode enables organizations to:

• Unify messy data into a trusted structure

• Detect and fix failure modes before deployment

• Iterate quickly without risking performance regressions

With Conode, Elxo didn't just deploy AI - they deployed it safely, confidently, and lightning fast.