Adoption of Artificial Intelligence (AI) Among Healthcare Practitioners of the University College Hospital, Ibadan Oyo State Nigeria

This study examined Adoption of Artificial Intelligence (AI) Among Healthcare Practitioners of the University College Hospital, Ibadan Oyo State Nigeria. Descriptive survey research design was employed, while purposive and simple random sampling procedures were employed to selected the sampled respondents. A self-designed questionnaire comprised AI Awareness, Usage, Benefits, Challenges and Coping Strategies subscales were used to obtain required data from the respondents. The obtained data were analysed with the aid of descriptive statistical embedded in SPSS. These consisted of simple percentage, mean and standard deviation, respectively. The survey revealed a predominantly female participation (64%) and a Christian majority (73%). The respondents' job roles were diverse, but nurses made up the largest group (53%). In terms of experience, most respondents (52%) had worked in the health sector for 1-5 years, indicating a relatively experienced but not long-tenured workforce. Healthcare personnel showed the highest awareness of Medical Imaging Analysis (AI Algorithm) with a mean score of (x̄ =2.96), followed by Artificial Narrow Intelligence (x̄ =2.89), and lower awareness of specialized AI applications like Luscii (x̄ =2.51), Expert System AI (x̄ =2.33), and Ada Health (x̄ =2.31), respectively. Artificial Narrow Intelligence was the most widely used AI application (x̄ =2.49), followed by Medical Imaging Analysis (x̄ =2.32), Ada Health (x̄ =2.19), Expert System AI (x̄ =2.16), and Luscii (x̄ =1.94), respectively, indicating varying levels of usage among healthcare personnel. The study revealed significant benefits of AI in healthcare, with AI-enhanced patient diagnosis and early detection scoring the highest (x̄ =3.50). Reducing workload for healthcare professionals (x̄ =3.35) and improving hospital administrative efficiency (x̄ =3.28) ranked second and third, respectively. Additionally, AI was found to increase job satisfaction (x̄ =3.13) and assist in more accurate drug prescription (x̄ =3.07). The empirical results revealed significant barriers to AI adoption in healthcare, with poor AI-enhancing infrastructure ranking first (x̄ =2.98), closely followed by concerns about data privacy and security (x̄ =2.96). High maintenance costs (x̄ =2.87) and inadequate training on AI usage (x̄ =2.85) also ranked among the top four barriers, highlighting the need for robust infrastructure, data security measures, and ongoing education. Finally, the concern that AI may replace human jobs in healthcare (x̄ =2.84) ranked fifth, emphasizing the need for addressing workforce anxieties about automation. The empirical outcomes revealed the top strategies for addressing AI adoption challenges in healthcare, with initiating training and development opportunities ranking first (x̄ =3.33), followed by designing simpler AI interfaces (x̄ =3.19), collaborating with private companies to improve infrastructure (x̄ =3.12), developing comprehensive data protection policies (x̄ =3.12), and utilizing open-source AI tools (x̄ =2.95). The study concluded that healthcare personnel acknowledged varying levels of awareness about and usage of different healthcare-oriented AI applications but attested that usage of AI application in this professional is more beneficial, while the effective usage was in part hindered by a series of challenges. The study recommended that the healthcare organisations in developing societies like Nigeria should prioritise investments in robust health-inclined AI infrastructure which aimed at enhancing the welfare of the needy citizens among others.