Artificial Intelligence for Clinic Notes and Letters in Surgical Outpatient Practice: Evolution, Outcomes, Assessment Methods, and Future Directions

Background/Objectives: Documentation burden is a major source of dissatisfaction among outpatient surgeons, and artificial intelligence (AI) tools—from general-purpose language models to purpose-built ambient scribes—have emerged as potential solutions. This narrative review examines their evolution, evaluates current evidence on effectiveness, and discusses assessment methods and future directions. Methods: Searches of MEDLINE, Scopus, Web of Science, and PubMed from inception to June 2026 identified 1843 records; following deduplication and PRISMA-guided screening, 20 studies met the eligibility criteria, supplemented by seven additional references identified via citation tracking, for 27 included sources. Findings were synthesised narratively across five domains: documentation efficiency, clinician wellbeing, patient experience, assessment methods, and implementation challenges. Results: Documentation efficiency showed the most consistent benefit, with reduced note-writing time reported across general, orthopaedic, and neurosurgical settings, though the most rigorous studies were derived from multispecialty and primary care cohorts rather than surgical practice. Wellbeing benefits were also frequently reported, but largely from uncontrolled designs and non-surgical populations. Documentation quality was more equivocal, varying by tool, specialty, and metric, with persistent concerns around factual inaccuracy, poor capture of informed consent, and unresolved questions of privacy, governance, and medicolegal liability. Conclusions: AI-assisted documentation shows a consistent efficiency benefit and probable wellbeing benefit, but surgery-specific evidence remains limited, documentation quality is tool- and specialty-dependent, and safety, governance, and equity concerns remain substantially unresolved. Future research should prioritise multicentre surgical trials with patient safety as a co-primary endpoint, specialty-tailored models, structured governance frameworks, and evaluation across linguistically diverse settings.