New research aims to ensure gender bias does not affect future AI healthcare systems

Glasgow University has won funding to examine the potential for gender bias in AI healthcare systems and find ways to ensure that AI-supported treatment remains equitable.


Recent advances in radar sensing technology could underpin a new generation of vital sign monitoring, experts said. A number of AI-enhanced vital sign monitoring systems, including Glasgow University’s £5.5m Healthcare QUEST, are currently in development.

The projects are exploring the potential of sensors to keep track of the rhythms of patients’ hearts and lungs without requiring them to wear monitoring devices or be tracked on video cameras, instead supported by less-invasive AI monitoring systems.

The AI will spot the signs of an unexpected change in heart rate or respiration, alerting aid if needed. Researchers said the technology could help vulnerable groups like older people live more independently at home or in assisted accommodation, as well as provide additional insight into the wellbeing of hospital patients.

Researchers from Glasgow University’s James Watt School of Engineering said that a ‘critically important’ consideration for any future radar-based health monitoring system is ensuring that its AI component is properly trained and equally capable of making the correct judgements without bias towards one gender of patients.

The project is supported by €9,500 (£8,200) in new funding from the Women and Science Chair at Université Paris Dauphine-PSL, supported by of the L’Oréal Foundation, Generali France, La Poste, Amundi and the Talan Group.

- The James Watt School of Engineering, Glasgow University

In a statement, research fellow in electronic and nanoscale engineering at Glasgow University and the project’s principal investigator, Dr Nour Ghadban, said: “We know that all kinds of human bias across race, class gender and more can be unwittingly incorporated into AI decision-making tools if the proper care isn’t taken when they are being trained on real-world data.

“It’s vitally important that we try to tackle these potential issues as early as possible to ensure that patient safety can be guaranteed, and male and female patients will receive the same high quality of care.”

Over the next 18 months, the researchers will aim to develop a new framework to balance gender-related behaviour in an AI monitoring system.

The research team said they will collect healthcare data from 30 male and 30 female study volunteers using radar sensors, used to then train a newly developed AI architecture, which will analyse the results of the radar monitoring.

Separate models will be trained on the male and female data, comparing performance and highlighting any biases in the AI’s performance, which can be adjusted for using statistical models and mitigation techniques.