Back

Yemeni university students public perceptions toward the use of artificial intelligence in healthcare: A cross-sectional study

Hatem, N. A. H.; Ibrahim, M. I. M.; Yousuf, S. A.

2024-02-28 health policy
10.1101/2024.02.27.24303457 medRxiv
Show abstract

The integration of artificial intelligence (AI) in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates the university students perceptions toward using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied for the comparison of variables. The response rate for the survey was 75%, with a sample size of 279. More than half of the participants (52%, n = 145) expressed their belief in AIs potential to reduce treatment errors in the future. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the "Benefits and Positivity Toward AI in Healthcare" and "Concerns and Fears" domains (p = 0.024) and (p = 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.