Title : Barriers and opportunities for AI adoption in cardiology: A cross-sectional study of cardiologists and cardiology residents in Sudan
Abstract:
Objectives: This study explores the current use, perceptions, and barriers to adopting artificial intelligence (AI) in cardiology practice among healthcare professionals in Sudan. It aims to identify the specific challenges facing AI integration in resource-limited settings, with an emphasis on cardiologists' readiness and infrastructural needs.
Methods: A cross-sectional, descriptive study was conducted using a self-administered questionnaire distributed to 125 cardiologists and cardiology residents in central hospitals across Sudan. A total of 59 participants (47.2% response rate) provided insights into their current use of AI tools, perceived benefits and challenges, and preferences for AI integration in clinical workflows. Data were analyzed using descriptive statistics to summarize the respondents’ sociodemographic characteristics, technology adoption behaviors, and AI tool usage.
Results: The findings revealed that 69.5% of participants favored integrating AI tools into Electronic Health Record (EHR) systems for ease of access. However, challenges such as inadequate IT infrastructure (71.2% lacking dedicated IT departments), financial constraints, and concerns over data privacy were major barriers. Only 28.8% of participants reported their facilities as prepared for AI implementation, and a notable preference emerged for checklist-format AI tools over complex, standalone systems.
Discussion: The study indicates that while interest in AI is high among Sudanese cardiologists, there are a number of critical barriers to its adoption. Financial and infrastructural limitations, coupled with concerns over patient data confidentiality, restrict the effective deployment of AI tools. The need for EHR integration and simplified tool formats reflects the participants' desire for practical, low-burden AI solutions.
Conclusion: Enhancing IT infrastructure, ensuring financial support, and fostering clinician-developer collaboration are critical to AI's successful implementation in Sudanese cardiology. Future research should focus on overcoming these specific challenges to facilitate AI adoption in similar resource-limited contexts.
