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What influences people’s trust in health-related Artificial Intelligence? An examination of human, contextual and technological factors

Year: 2025
Type: dissertation
Abstract: Artificial Intelligence (AI) has the potential to transform healthcare by augmenting prognosis, diagnosis and enabling personalized medicine through data analytics. However, the implementation of AI in healthcare faces challenges including data privacy, accountability, transparency, and fairness. Central to these challenges is the issue of trust, which significantly affects the adoption of AI. This thesis investigated the factors influencing trust in health-related AI through an interdisciplinary approach. Five studies using qualitative and quantitative methods were conducted. Study 1 [S1] was a systematic review which mapped out existing evidence and generated a logic model that categorises influences on trust into human-, AI-, and context-related factors. S2, a Public Engagement project, developed a toolkit reflecting people’s information needs for deciding whether to trust AI in public health, refining the logic model. The second part of the thesis examined the social construction of AI through a semi-automated content analysis (S3) and a discourse analysis (S4) of British and German news outlets. These studies suggest that differences in AI discourse are more related to the political leaning of the outlet rather than the country, with similar discursive strategies used in both. S5, a Factorial Survey Experiment, examined which factors from the logic model predict trust in a hypothetical AI system. Results indicated that knowledge, expectations, age, and stakes predict trust in AI. Different types of transparency (interpretability, fairness, training data) also predicted trust, with impact varying depending on whether the information about the AI system is favourable. Collectively, the studies suggest that transparency, AI companies, the media, knowledge, expectations, and the tendency to compare AI to humans are central factors influencing trust in health-related AI. These findings can be used to inform research agendas, transparency guidelines, as well as initiatives aimed at increasing AI literacy and developing ways to support people in their trusting decisions.
Language: English
Field: Medicine
Open Access status: green
OpenAlex ID: W6997349247
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