ALRY

Context problem: in large hospitals, more than 10,000 tests per day are performed; this means that a nurse or doctor spends approximately 10min per test, or 100,000 per day/person. The exam results are hard to interpret (only text and numbers).

Current State: Visual interpretation of text (taking a nurse approximately 10 minutes per analysis, without AI assistance). The patient could have access to a tailored interpretation of the exams in non-medical terms.

Objective: To assist physicians and patients in interpreting medical exam results using AI and UI Generative.

Use Cases:

’. Single Blood Test Interpretation: Assist with basic interpretation.

2. Multiple Blood Test Interpretation: Assist with interpretation and identify patterns in blood levels.

3. Knowledge Base Integration: Assist with interpretation, identify patterns, and leverage a scientific knowledge base to highlight potential health risks for the physician, backed by medical literature.

4. Interpretation of ultrasound medical exams, transforming text and number in visual elements

Example: With 5 consecutive blood tests, the AI could detect a trend and suggest potential conditions to the physician.

Solution: Provide seamless access to interpretations, consultations, and detailed exam insights through a platform where the user (medical, nurse or patient) inputs a PDF (e.g., hemogram result) and gets the analyzed result of the ina generative UI interface. You can input many hemogram result to the AI recognize pattern and point out disease risks and attention. It also helps physicians to interpret the results in a user-friendly interface, and the patient can also have a non-medical interpretation of this result.

Impacts:

1. Time Saving: Generative Interfaces (Generative UI), the interpretation time for a normal complete blood count (CBC) can drop from 10 minutes to less than 30 seconds.

2. Quality of the interpreation

3. User friendly interface of the blood results

Other Benefits: medical interpretations of the exams are more optimized and interactive; a more user-friendly interface for nurses, doctors and patients of the medical exam results.

Technology used: Stack: Next.js (UI) + CopilotKit + agente Python hemogram_agent (Gemini) na porta 8123

Escalability: this solution can be applied in human medicine and veterinary medicine context.

Roadmap:

Phase 1: reliable hemogram result using Generative UI
Phase 2: Multi-exam UX; Syncy Agent Panel; Parser + Feedback UI + Improvements
Phase 3: Multi-repo platform; exam history; saving pdf’s in a database;
Phase 4: Agent Analyzer; Dynamic UI; API to diagnostic centers and private hospitals