TokenWave AI Pte. Ltd.
Get Start

Explainable AI That Helps Clinicians Read Medical Images

Client:
TokenWave AI (In-House R&D)
Type of Work:
Medical AI / Computer Vision
Industry:
Healthcare
Stage:
Research-Based Solution
At a Glance

Case Study Overview

The Challenge

Growing scan volumes and a shortage of radiologists delay reports and risk missed early signs.

Our Solution

An explainable AI system that pre-screens medical images and highlights likely conditions.

Technology

Deep CNNs, transfer learning, image pre-processing and visual explainability (heatmaps).

The Outcome

Faster triage and a trustworthy second opinion, with doctors always in control.

Executive Summary

Medical imaging is central to modern diagnosis, but the number of scans produced every day has grown much faster than the number of trained radiologists. The result is long reporting queues, overloaded specialists and a real risk that subtle early signs of disease are missed.

TokenWave AI built an explainable medical image analysis system that supports clinicians in detecting conditions such as pneumonia, COVID-19 related lung changes, lung cancer, kidney anomalies and diabetic retinopathy. The system does not replace the doctor - it pre-screens images, prioritises urgent cases and shows exactly which regions of an image influenced each prediction.

Background

The Industry Context

Chest X-rays, CT scans and retinal images are among the most frequently performed diagnostic tests. Each one requires careful expert review, and many conditions - especially in early stages - present only subtle visual changes.

In smaller cities and rural areas, access to specialist radiologists is especially limited. Images are often sent elsewhere for reporting, adding days to a process where early treatment can make a significant difference to outcomes.

project
project
The Challenge

The Problem We Set Out to Solve

1
Radiologist Shortage

Too few specialists for the volume of scans, causing delays in reporting and, consequently, in treatment.

2
Subtle Early Signs

Early-stage disease often shows only faint patterns that are easy to miss under heavy workloads and fatigue.

3
Multiple Conditions and Modalities

X-rays, CT scans and retinal images each require different expertise and different analysis approaches.

4
Trust in AI Decisions

Clinicians will not rely on a black-box system that gives answers without showing the reasoning behind them.

Why Existing Approaches Fall Short

  • Manual review alone cannot keep pace with growing imaging volumes.
  • Many AI tools provide a label without an explanation, limiting clinical adoption.
  • Models trained for one condition rarely generalise to others.
  • Outsourced reporting adds delay and cost for smaller facilities.

What Was at Stake

  • Patient outcomes: delayed diagnosis of conditions like lung cancer or pneumonia directly affects treatment success.
  • Operational load: reporting backlogs strain radiology teams and increase the chance of error.
  • Access to care: patients in under-served regions wait longest for results.
  • Preventable vision loss: diabetic retinopathy often goes undetected until it is advanced.
Our Approach

How We Approached the Problem

We followed a structured, problem-first process - understanding the real-world problem before choosing the technology, and validating every stage before moving to the next.

Clinical Problem Framing

We identified high-impact, image-based conditions where early detection matters most and defined the system's role clearly: decision support and prioritisation, with final diagnosis always made by a qualified professional.

Data Preparation

Medical image datasets were curated for each condition. Images were standardised, enhanced and augmented to account for differences in equipment, contrast and patient positioning.

Model Development

Deep-learning models were developed for each imaging task, using transfer learning from pre-trained networks to achieve strong performance with limited medical data.

Explainability

Visual explanation techniques were integrated so every prediction is accompanied by a heatmap highlighting the image regions that drove the result - making the AI's reasoning reviewable by clinicians.

Validation and Workflow Design

Models were validated on held-out images, and the output was designed to fit naturally into a clinician's review process: probabilities, highlighted regions and a priority flag.

The Solution

How the Solution Works

An explainable deep-learning system that analyses X-rays and medical scans, flags likely diseases with probability scores and shows doctors why.

Solution Workflow

STEP 01

Medical image is uploaded

STEP 02

Image is standardised and enhanced

STEP 03

Deep-learning model analyses it

STEP 04

Disease probabilities are produced

STEP 05

Heatmap highlights key regions

STEP 06

Clinician reviews and decides

Key Capabilities

Multi-Condition Detection

Supports pneumonia, COVID-19 lung changes, lung cancer, kidney anomalies and diabetic retinopathy.

Probability Scores

Provides a likelihood for each condition rather than a simple yes or no.

Visual Explanations

Heatmaps show which regions of the image influenced the prediction.

Case Prioritisation

High-risk images can be flagged for earlier review.

Doctor-in-the-Loop

Designed as a second opinion - clinicians make every final decision.

Modular Design

New conditions and imaging types can be added as separate models.

Technology & Techniques

PythonDeep Learning (CNNs)Transfer LearningMedical Image Pre-processingData AugmentationExplainable AI (Heatmaps)Secure Upload Interface
Results

Business Impact & Outcomes

Faster Triage

Urgent cases can be identified and prioritised as soon as images arrive.

Explainable Results

Clinicians can see why the AI made a prediction and verify it themselves.

Wider Access

Smaller facilities gain AI-assisted screening without a full-time specialist on site.

“In healthcare, an AI prediction is only useful if a doctor can trust it. That is why every result our system produces comes with a visual explanation.”
— TokenWave AI Project Team

Who Can Benefit

  • Diagnostic centres for reducing reporting backlogs and turnaround time.
  • Hospitals for prioritising critical cases in emergency and inpatient settings.
  • Telemedicine platforms for adding AI-assisted screening to remote consultations.
  • Health-tech startups for building screening products on a proven foundation.
Looking Ahead

Key Learnings & What's Next

Key Learnings

  • Explainability is not optional in healthcare - it is what makes clinical adoption possible.
  • Careful pre-processing is essential because image quality varies widely across equipment.
  • Positioning AI as decision support, not a replacement, builds trust with clinicians.

What's Next

  • Extending support to additional imaging modalities and conditions.
  • Integration with hospital PACS and reporting systems.
  • Structured draft reports to further reduce reporting time.
  • Clinical validation partnerships with healthcare institutions.

This case study describes an in-house research and development project by TokenWave AI. Outcomes are described qualitatively; actual performance depends on the data, environment and scale of each deployment. Want to apply this solution to your business? Book a free consultation.

More Case Studies

Agriculture
Crop Disease, Caught Early
 Education & Assessments
Trustworthy Online Exams
 Retail, Brands & Services
Hearing the Customer Clearly
 Banking & Fintech
Payment Fraud, Flagged Fast
 Cybersecurity & IT
Network Attacks, Spotted Early
 Transportation & Logistics
Driver Fatigue Alerts

Have a Process That Should Run Itself?

Tell us about it in a free, no-obligation working session. We will show you where an agent fits, what it would take and how you would measure the result.

Book a Free Session

Join our mailing list for field notes on applied AI, delivered occasionally.

Company
Services
Resources
Follow Us

Copyright © TokenWave AI Pte. Ltd. All rights reserved.