Ambient Artificial Intelligence in Hand Surgery: Insights, Pearls, and Pitfalls From Real-World Use

Abstract

BACKGROUND: Documentation burden is a major contributor to surgeon burnout, particularly in high-volume outpatient specialties such as hand surgery. These encounters often involve multiple concurrent diagnoses, procedural planning, and longitudinal care. Ambient artificial intelligence (AI) tools, which passively transcribe and summarize clinical encounters in real time, have the potential to streamline note generation and reduce cognitive load. METHODS: Following institutional rollout of a commercially available ambient AI tool in early 2025, the system was adopted across all outpatient visits in a hand surgery clinic. Observations were collected regarding workflow adaptation, documentation quality, and perceived cognitive impact. To assess note characteristics, 14 carpal tunnel syndrome visit notes (7 AI-generated and 7 surgeon-written) were compared by word count, Flesch-Kincaid readability score, and inclusion of social and medical history. RESULTS: AI-generated histories were longer and exhibited greater linguistic complexity compared with surgeon-written notes. Surgeon-written assessments were longer, more readable, and more consistent in structure and tone. Social history was documented more frequently in AI-generated notes. All AI-generated assessment and plan sections followed a structured, problem-based format. CONCLUSIONS: Ambient AI documentation can support the creation of more structured and contextually rich clinical notes in hand surgery settings. However, successful integration requires active clinician engagement and workflow alignment to balance efficiency with clinical accuracy. These findings highlight both the promise and the practical considerations of incorporating ambient AI into surgical documentation workflows.

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