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The age of artificial intelligence

02 May 2023
Opinion
02 May 2023
Volume 15 · Issue 5

Artificial intelligence (AI) is gradually integrating into various sectors such as finance, transportation, energy and education. Although AI is in its infancy in healthcare, it is still being used in many ways, including medical imaging, chatbots, diagnosis, treatment, and telephone triage in an ambulance setting. The introduction of AI has given rise to ethical concerns—particularly about how data are gathered and used (Gerke et al, 2020).

The key attributes of AI are its ability to analyse and compare vast datasets and predict likely outcomes, hence its integration into patient triage and assessment systems. To achieve genuine impartiality and autonomy in the realm of AI, datasets utilised by such systems must possess analogous qualities (González-Gonzalo et al, 2022). Norori et al (2021) highlight statistical and social bias within healthcare datasets.

Statistical bias occurs when the distribution of a particular dataset does not accurately represent the distribution of the population which, in turn, causes algorithms to produce inaccurate outputs, typically at the expense of lower socioeconomic groups. The medical industry has been recognised as being susceptible to biases, which can be challenging to detect and measure, with numerous reports of discrimination against vulnerable groups (FitzGerald and Hurst, 2017; Rivenbark and Ichou, 2020). This is ultimately the data that AI draws from. Vyas et al (2020) suggest that healthcare algorithms often do not offer a rationale for why racial and ethnic differences might exist, along with the assumption that the data gathered could be outdated; this further perpetuates the risk of algorithmic bias.

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