Generative artificial intelligence–assisted medical self-care and associated factors among undergraduate students at Arsi University, South-Eastern Ethiopia

@article{adem2026generative,
  title = {{Generative artificial intelligence–assisted medical self-care and associated factors among undergraduate students at Arsi University, South-Eastern Ethiopia}},
  author = {Jibril Bashir Adem and Anas Ali Alhur},
  journal = {PLOS Digital Health},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pdig.0001748},
  url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001748},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - Generative artificial intelligence–assisted medical self-care and associated factors among undergraduate students at Arsi University, South-Eastern Ethiopia
AU  - Jibril Bashir Adem
AU  - Anas Ali Alhur
PY  - 2026
DA  - 2026/09/24
JO  - PLOS Digital Health
DO  - 10.1371/journal.pdig.0001748
UR  - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001748
AB  - The rapid expansion of social media and the availability of Generative Artificial Intelligence (GenAI) technologies like ChatGPT and Gemini have significantly increased access to personalized health information. This has led to a rise in self-directed health behaviors such as self-diagnosis and self-medication. While GenAI can support informed self-care decisions, over-reliance on these technologies may result in incorrect self-medication and delayed consultations with medical professionals if not managed appropriately. However, there is a notable lack of systematic empirical data regarding GenAI-assisted medical self-care practices, particularly in Ethiopia. This study aimed to assess the prevalence of GenAI-assisted medical self-care and its influencing factors among undergraduate students at Arsi University in South-Eastern Ethiopia. A cross-sectional study was conducted with 414 randomly selected students using a self-administered questionnaire. Data were collected through Kobo Toolbox, and descriptive analyses alongside binary logistic regression were performed to identify factors associated with GenAI-assisted self-care. The study achieved a response rate of 97.87%, with participants having a mean age of 22.2 years. Results showed that 70.05% of students engaged in GenAI-assisted medical self-care. Factors positively associated with this behavior included being a second-year student (AOR=2.98), possessing good digital health literacy (AOR=1.71), awareness of GenAI technologies (AOR=2.18), a positive attitude towards these technologies (AOR=1.63), and recent health facility visits (AOR=2.46). Conversely, good health-seeking behavior (AOR=0.61) and infrequent health facility visits (AOR=0.49) were linked to lower odds of engaging in GenAI-assisted self-care. The findings suggest a high prevalence of GenAI-assisted self-care among university students and highlight the need for guidelines and training on digital health literacy for effective and safe technology use in healthcare. Author summary: Patient engagement with self-directed health behaviors, such as self-diagnosis, disease-related information seeking, and the use of self-medication as a primary strategy or as a substitute for formal medical care, has significantly increased in parallel with advancements in digital technology and Generative Artificial Intelligence (GenAI) tools. Empirical evidence suggests that health science professionals, students, and those with higher levels of education and professional accomplishments engage in higher rates of medical self-care, due to their greater access to medical information through peer networks, scientific literature, and pharmaceutical companies. We employed a cross-sectional survey among undergraduate students at Arsi University using self-administered questionnaires to assess the use of GenAI–assistance for medical self-care and its associated factors. The study demonstrated that GenAI-assisted medical self-care is highly prevalent among higher education students. The study also found that individual, technological, and behavioral factors influence the adoption of GenAI-assisted medical self-care. Given a growing dependence on innovative technologies such as large language models and GenAI, universities, healthcare professionals, and policymakers should enhance digital health literacy and provide guidelines on the appropriate and safe use of GenAI in health care, while encouraging students to seek medical advice for conditions that require clinical evaluations.
SN  - 2767-3170
ER  -