Healthcare systems are increasingly turning to ambient Artificial Intelligence (AI) scribes to reduce documentation burden and lighten clinicians’ cognitive load. In this brief research report, we introduce MediVoice, an ambient AI scribe developed and implemented within the National University Health System, Singapore. MediVoice was piloted across multiple clinical settings and rapidly evaluated through Plan–Do–Study–Act cycles. Doctors, nurses, and allied health professionals assessed its usability, accuracy, workflow fit, and potential time savings. Real-time feedback informed iterative refinement, enabling organisational learning and reinforcing the value of experimentation during early AI adoption. Broader deployment required leadership engagement, an AI community of practice, role-specific training, user champions, recognition of its value, and supporting digital and organisational infrastructure. Looking ahead, routine use will require integration with the electronic medical record, enhanced speech recognition capabilities, and robust AI governance frameworks. MediVoice’s trajectory shows that while ambient AI scribes can offer meaningful benefits, success requires more than technical capability. Effective implementation needs continuous adaptation, workflow alignment, cross-professional engagement, governance, and organisational readiness. This case study offers practical lessons for health systems seeking to introduce ambient AI tools within clinical environments.
Co-designing animated videos to explain large language models and their use in healthcare and research
IntroductionThe increasing development of large language models (LLM) in healthcare research is taking place without patient and public involvement and engagement (PPIE). Part of the