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AI-Based Diagnostic Reporting System

An AI-powered reporting layer that converts structured scan findings into accurate, formatted diagnostic reports — reducing preparation time by 80% and tripling radiologist throughput.

80%

Reduction in report preparation time

Increase in radiologist throughput

95%+

Report accuracy validated by clinicians

< 2 min

Average time from scan to structured report

Client Overview

A multi-site diagnostics provider operating across urban and semi-urban locations, handling thousands of radiology scans monthly. The client operated a legacy reporting workflow where radiologists manually drafted reports for each scan — a process that was time-intensive, inconsistent in structure, and difficult to scale with growing scan volumes.

Business Challenge

Report preparation consumed the majority of each radiologist's time, with a single diagnostic report taking 15–25 minutes to draft manually. Across 300+ daily scans, this created a systematic bottleneck that delayed patient results and reduced overall clinical capacity.

  • 300+ daily scans requiring manual report drafting
  • 15–25 minutes per report, creating patient delays
  • Inconsistent report structure across radiologists
  • No structured data layer for downstream analytics or compliance

Strategic Approach

Rather than building a fully autonomous system, we designed an AI-assisted workflow that retained radiologist oversight while removing the repetitive drafting burden. The core principle: AI generates structured, editable draft reports; radiologists review, correct, and sign off.

This hybrid approach maximised adoption, maintained clinical accountability, and allowed the system to improve over time through radiologist corrections fed back into the model as training data.

Implementation

01

Discovery & Workflow Mapping

Mapped existing reporting workflow across 3 sites. Identified 12 distinct report templates in use. Established baseline metrics for report time, error rate, and radiologist capacity.

02

NLP Pipeline Development

Built a structured extraction pipeline to parse scan metadata, machine-generated findings, and radiologist shorthand into a standardised data schema.

03

AI Report Generation Layer

Integrated GPT-4 API with domain-specific system prompts, constrained to approved clinical terminology. Output structured report drafts in the client's standard template format.

04

Radiologist Dashboard

Built a React-based review interface where radiologists receive AI-drafted reports, make inline edits, and sign off — replacing blank-page drafting with structured review.

05

Feedback & Iteration Loop

Corrections made by radiologists were logged and used to refine prompt templates and re-evaluate output quality bi-weekly, improving accuracy continuously post-launch.

Technology Stack

Python (NLP pipeline)
OpenAI GPT-4 API
FastAPI (backend services)
PostgreSQL (structured records)
React (radiologist dashboard)
Docker & AWS ECS (deployment)

Measurable Results

80%

Reduction in report preparation time

Increase in radiologist throughput

95%+

Report accuracy validated by clinicians

< 2 min

Average time from scan to structured report

Key Takeaways

  • AI-assisted reporting removes repetitive drafting without replacing clinical judgment.
  • Structured NLP output enables downstream analytics and compliance documentation.
  • Modular architecture allows integration with existing PACS and HIS systems.
  • Physician adoption improved when AI suggestions were framed as editable drafts, not final outputs.

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