Future of artificial intelligence in healthcare: Knowledge, attitudes, and acceptance by healthcare workers in Ghana- A quantitative cross-sectional case study
Lily Yarney, Judith Maame Tanoa Yelbert, Herman Nuake Kofi Agboh, Joel Yarney, Robert Mayfield YawsonObjective
Artificial Intelligence (AI) is a solution to healthcare workforce shortages and system inefficiencies, yet its adoption in many low- and middle-income countries remains limited, and the evidence on its usefulness uneven. This study assessed selected healthcare workers’ perceived AI knowledge and attitudes towards AI, and intentions to accept AI in a tertiary healthcare setting in Ghana.
Methods
Guided by the Technology Acceptance Model (TAM), a descriptive cross-sectional study was conducted among 266 healthcare professionals at the University of Ghana Medical Centre. Data were collected using a structured questionnaire. Internal consistency was assessed using Cronbach’s alpha (≥0.70 acceptable). Composite scores for self-rated knowledge, attitudes, and perceptions were computed and categorized using mean cut-off values. Descriptive statistics were generated, and inferential analyses included Pearson correlation and one-way ANOVA, with statistical significance set at p < .05.
Results
Participants showed moderate but uneven knowledge of AI, with limited familiarity in areas such as machine learning and formal training. Despite this, attitudes toward AI were strongly positive, with most participants recognizing its potential to improve patient outcomes. AI knowledge was significantly associated with favourable attitudes (r = .46, p < .001), trust in AI systems, and willingness to work with AI technologies. Attitudes also differed significantly by years of professional experience, with participants having 1–3 years of experience reporting the most favourable attitudes, F(3, 262) = 4.12, p = .007. AI knowledge differed significantly across professional cadres, with pharmacists and medical doctors reporting higher knowledge scores than several other professional groups, F(4,261) = 3.89, p = .001. However, ethical concerns, lack of trust, and insufficient training emerged as key barriers.
Conclusions
With the respondents’ general positive attitudes and intentions to accept AI, strengthening AI literacy, practical training, and contextually appropriate ethical governance may strongly support sustainable AI integration in Ghana’s healthcare settings.