AI has great potential in healthcare, but existing algorithms risk perpetuating and worsening existing biases, particularly racial and ethnic disparities. This master’s thesis, from the Faculty of Engineering of the University of Porto and developed with researchers at MIT, looks at these issues through a health equity lens. It reviews challenges such as sharing clinical data, selection bias, biased measurements from medical devices, and common pitfalls in developing AI.
The thesis then proposes three research frameworks, tested with a large intensive care database. The first found that patients with sepsis from racial and ethnic minority groups were less likely than White patients to receive mechanical ventilation and medication to raise blood pressure. The second found no substantial differences between groups in the effect of these treatments on outcomes such as mortality. The third focused on pulse oximeters, which are less accurate in people with darker skin, and showed that machine learning can be used to correct their readings, although this still needs to be validated in other databases.
Distinguished in the 2023 IEEE Portugal Section Outstanding MSc Thesis Awards, this work highlights the need to place health equity at the forefront of health research.
Learn more about this thesis here: https://repositorio-aberto.up.pt/handle/10216/150994
Reference:
Matos, J. (2023). Research frameworks towards health equity [Master’s thesis, Faculdade de Engenharia da Universidade do Porto]. Repositório Aberto da Universidade do Porto.
