NeuroLens — AI-Assisted Radiology Report Reader
Transformer-based NLP parses radiology reports, highlights urgent findings and creates structured EMR-ready summaries with guideline prompts to cut read time and oversight risk.
Overview
NeuroLens is an AI-powered natural language processing platform that reads long, technical radiology reports, extracts clinically important entities and relationships, and condenses them into structured, EMR-ready summaries. The system highlights urgent findings and suggests guideline-aligned next steps so that radiologists and treating clinicians can prioritize actions quickly. By surfacing key information without requiring a full read-through on every report, NeuroLens aims to reduce interpretive burden while improving consistency and follow-up planning.
Clinical Problem
Workloads and report length combine to make radiology reading both time-intensive and cognitively demanding. Important details can be missed or delayed, particularly when reports span multiple anatomical sites, prior comparisons, and incidental findings. Variability in phrasing across different reporters also complicates downstream EMR usage and clinical decision-making. A standardized, AI-assisted way to parse, highlight, and structure content can accelerate interpretation and reduce oversight risk while feeding cleaner data to hospital systems.
Methodology
- Train on anonymized reports and outcomes to learn anatomy, pathology, and severity markers.
- Extract entities and relations; rank by urgency and clinical relevance; map to guideline-aligned recommendations.
- Generate compact, structured summaries for EMR with links back to key sections in the original text.
Tech Stack
Equipment
Expected Outcomes
Algorithm training ongoing; pilot with radiologists planned for validation and workflow fit.