Mapping Global Discourses on Trustworthy Artificial Intelligence in Healthcare: A Longitudinal Mixed-Methods Text Mining Approach (Preprint)

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Abstract
BACKGROUND Implementing artificial intelligence (AI) in healthcare requires more than technological capability, as successful adoption depends on trust, organisational readiness, clinical workflows, governance, and patient safety. However, evidence describing how healthcare professionals collectively perceive these implementation challenges remains limited. OBJECTIVE This study aims to investigate the dominant implementation challenges of AI in healthcare through professional discourse and to develop a comprehensive, empirically grounded conceptual implementation framework. METHODS A mixed-methods study was conducted using longitudinal discussion data from an international healthcare quality learning community between September 2024 and July 2026. Following data cleaning, 799 discussion posts were analysed using descriptive text analytics, keyword co-occurrence analysis, latent topic modelling, and systems-based interpretation to identify core implementation barriers and synthesize a new framework. RESULTS Trust emerged as the primary implementation concern, associated with data governance, transparency, clinical decision-making, workflow integration, and safety. Topic modelling identified four principal domains: trustworthy implementation, patient safety, data governance, and clinical integration. Systems-based interpretation showed challenges concentrated within technological and workflow dimensions, with organisational factors receiving less attention. These insights formed the Global Trust-Centred Artificial Intelligence Implementation Framework (GTAIF), positioning trust as the core mechanism linking technology, readiness, governance, and quality. CONCLUSIONS Successful implementation of artificial intelligence should be understood as a sociotechnical transformation rather than a purely technological endeavour. The proposed framework provides an empirically grounded systems perspective that may support healthcare leaders and policymakers in implementing safe, trustworthy, and sustainable AI within healthcare organisations. CLINICALTRIAL Not applicable.

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