AI Diagnostic Assistant
Assisting radiologists in detecting early signs of anomalies in X-rays with Computer Vision.
Role
Product Lead
Timeline
24 Months
Team
4 Research Scientists, 3 Engineers, Clinical Partners
Context & Problem
The Context
Radiologists are overworked, leading to burnout and potential diagnostic errors.
The Problem
High volume of scans means less time per patient. Subtle anomalies are easily missed in early stages.
Opportunity & Vision
"An AI 'second pair of eyes' that highlights potential regions of interest, prioritizing the worklist for radiologists."
Data & AI Opportunities
Convolutional Neural Networks (CNNs) trained on large datasets of annotated X-rays to detect specific pathologies.
System Architecture
Edge deployment for privacy and speed. DICOM integration. FDA clearance workflow built into the development process.
Data Ingestion
Kafka / IoT Sensors
AI Processing
TensorFlow / Spark
API Layer
FastAPI / GraphQL
Client App
React / Mobile
Tradeoffs & Prioritization
We focused on high sensitivity (recall) to ensure no potential issues were missed, accepting a higher false positive rate which the radiologist could easily dismiss.
Execution & Collaboration
Partnered with 3 major hospitals for data and validation. Navigated complex regulatory landscape (HIPAA, FDA).
Metrics & Impact
Improved detection rate of early-stage nodules by 20%. Reduced average review time per scan by 30%.
Reduction
In unplanned downtime
ARR
Generated in 18 months
Accuracy
On critical failure modes
Lessons Learned
"User trust is paramount in healthcare. Explainability (Grad-CAM heatmaps) was just as important as model accuracy."