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Awarded Best Application Paper Award

internationally by IEEE/RSJ IROS (2022)

Using AI & Robotics for
Medical Examinations

As the population grows, healthcare systems such as the NHS become increasingly saturated. An ageing population also means that more health checkups are needed, so that health issues can be identified at early stages when they are easily treatable.

To tackle this issue, more emphasis and resources should be put on the automation of common regular examinations.

This project presents the research and development of a proof-of-concept AI-enabled device to examine the state of underlying organs from the comfort of your home.

Data Analysis

 Pilar Zhang Qiu | Jacob Tan | Oliver Thompson | Ben Cobley 

Machine Learning


Relieve Pressure from Healthcare Systems

Healthcare systems such as the NHS suffer from long backlogs. Automating regular appointments can help ease the system.

Accelerate Research through Big Data

Increased number of data points can help generate more accurate AI models to accelerate medical research.


for the Elderly

More than 1.45M elderly in the UK find it difficult to travel to hospital appointments[1]

Remote devices can facilitate access medical care.


Stage 1

Problem Definition

Literature Review

Robot Ideation

Robot Prototyping


Stage 2

Experiment Design

Jig Prototyping

Data Collection

Stage 3

Data Processing

Data Analysis

ML Models


System Summary

Robotic Device:

Percusses Phantoms

The device impacts the silicone phantoms, which represent the human abdomen.

Data Collection:

Audio Response

The acoustic data resulting from the impact is collected for 15 different abdominal structures, with a total of 7,500+ recordings saved.

AI Models:

Estimating Organ State

Predicted organ consistency and "size" using Gaussian Mixture Modelling (GMM) and Neural Networks (NN).


The AI models showcased



in identifying 3 different organ consistencies

and 5 different organ "sizes".

We focused on

Explainable AI.

The biggest issue with AI is that it can be a "blackbox".

Our innovative method presents a solution for understandable AI-enabled research.

at the highly-influential IEEE/RSJ IROS 2022 Conference (Kyoto, Japan) for our contribution to the field.


We were honored to receive the


Best Application Paper Award


Journal Paper

Journal Paper

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