Vibe Coding to Democratize Dementia mHealth Development: Preliminary Findings

BACKGROUND: Vibe coding tools enable software generation through natural language prompts powered by generative artificial intelligence, substantially lowering the technical barrier to application development. Their potential to empower health professionals without programming backgrounds is promising, yet remains largely unexplored in the literature. OBJECTIVE: To analyze whether vibe coding tools effectively bridge the gap between non-technical health professionals and digital health application development, and to identify the enablers, barriers, and risks associated with their use. METHODS: This study used a design science approach to create and preliminarily evaluate digital health artifacts. In a 3-hour in-person workshop, medical students used a vibe coding platform to build mHealth prototypes for dementia care challenges. The process included problem identification, ideation, prototyping, presentation, and reflection. Data came from participant observation, artifact review, and a post-workshop satisfaction survey. RESULTS: All three groups successfully developed functional prototypes within the allotted time. Key enablers included conversational accessibility, immediate visible results, and direct clinical applicability. Critical barriers included unfamiliarity with health data privacy regulations, absence of security measures in all prototypes, and a tendency to define excessively broad problem scopes. Notably, no group implemented user authentication or data encryption. CONCLUSIONS: Vibe coding effectively brings health professionals closer to digital application development, but this democratization is not sufficient on its own. It must be accompanied by training in data security, health informatics and AI fundamentals.