AI AgentsHealthcareDeep LearningDiagnostics

AI-Assisted Medical Imaging — Helping Radiologists Catch What Gets Missed

Industry: HealthcareLocation: UKDuration: 16 weeksTeam: 5 engineers + 2 medical advisors

“An imaging center network was struggling with growing backlogs and overworked radiologists. We built an AI assistant that pre-screens scans, flags anomalies, and prioritizes urgent cases — helping doctors work faster and catch findings that might be missed during long shifts.”

The Results

~85%↓

Wait Time

Patient wait time for results dramatically reduced

3-4x

Throughput

Radiologists review significantly more scans per day with AI assistance

97%+

Accuracy

AI-assisted diagnostic accuracy across supported pathology types

~80%↓

Re-reads

Significant reduction in scans requiring re-reads

The Problem

What Was Going Wrong

A multi-location imaging center had radiologists reading far above recommended daily limits. Backlogs were growing, wait times for results were unacceptable, and the risk of missed findings was increasing. Recruiting additional radiologists was proving nearly impossible.

Weeks-long wait times for scan results — patients suffering in uncertainty

Radiologists overloaded beyond safe reading limits

Risk of missed findings increasing with workload

Quality concerns requiring frequent re-reads of scans

Extreme difficulty hiring experienced radiologists

The Solution

What We Built

We built an AI diagnostic assistant that pre-screens every scan, flagging anomalies with confidence scores and visual heatmaps. Radiologists review AI-flagged cases first, dramatically reducing turnaround time while adding a safety net for findings that might otherwise be missed.

Deep learning model trained on a large dataset of anonymized medical images across multiple pathology types

Heatmap overlay showing exactly where the AI detected anomalies

Priority queue system — high-confidence findings go to the front of the review line

Fully compliant architecture with end-to-end encryption (HIPAA/NHS standards)

Seamless integration with existing PACS systems — zero workflow disruption

Tech Stack

PyTorchMONAIFastAPIReactPostgreSQLDICOMAzure HealthDocker

The Transformation

Before vs After

Before

Weeks-long wait for results

After

Days-long turnaround

Before

Overloaded radiologists

After

AI-assisted efficient workflow

Before

Increasing missed finding risk

After

AI safety net catches anomalies

Before

Frequent re-reads needed

After

Dramatically fewer re-reads

“This isn't about replacing doctors. It's about giving us a safety net. I catch things now that I know I would have missed at the end of a long day. This tool has genuinely made a difference.”

— Chief Radiologist

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