Bridging the AI-literacy gap in healthcare: a qualitative analysis of the Flanders case-study (Preprint)

BACKGROUND The integration of Artificial Intelligence (AI) in healthcare is advancing rapidly, promising to enhance clinical decision-making, streamline administrative tasks, and personalize patient care. However, many healthcare professionals report a lack of confidence in understanding, critically evaluating, and ethically applying AI technologies. In regions like Flanders, Belgium—recognized for innovation yet facing moderate lifelong learning participation—these challenges are pronounced, especially amid an aging healthcare workforce and resource disparities between professions. OBJECTIVE This study aimed to explore the requirements, obstacles, and prospects of AI adoption among healthcare professionals, and to identify the specific training priorities needed to bridge the AI-literacy gap in clinical practice in the Flanders region. METHODS A multi-stage qualitative methodology was employed. First, 15 semi-structured interviews with key informants were conducted to inform the survey design. Then, a survey was distributed to healthcare professionals across Flanders, gathering 134 valid responses. Finally, three focus groups involving 39 participants were conducted to co-interpret the survey findings. Thematic analysis and descriptive statistics were used to synthesize insights across stages. RESULTS Healthcare professionals recognized AI’s potential to reduce administrative burdens and enhance clinical care but reported low self-perceived AI literacy, especially among older and non-physician staff. Interest in AI training was high, particularly for practical applications and basic AI knowledge, rather than technical coding or standalone ethics courses. Differences emerged based on occupation, age, and perceived job security. Nurses and younger professionals were especially concerned about the risks and opportunities of AI adoption. A lack of legally approved AI tools and practical hands-on training were identified as major barriers. Focus group discussions highlighted disparities in access to AI training between doctors and nurses, skepticism about private-sector-led courses, and the need for hospital management support in facilitating AI education. CONCLUSIONS A one-size-fits-all approach to AI training in healthcare is inadequate. Training programs must be stratified by occupation, age, and resource availability, emphasizing immediate practical applications while embedding ethical considerations within broader curricula. Addressing barriers to training accessibility and clarifying regulatory frameworks will be crucial to scaling AI integration in healthcare systems, starting in Flanders and potentially informing broader European initiatives under frameworks like the EU AI Act.