FROM PEN TO PIXEL: CAN HEIDI AI OUT-WRITE THE HUMAN HAND IN ORTHOPAEDIC LETTERS?

Background and Aims Accurate clinical documentation is fundamental to orthopaedic practice, yet adherence to British Orthopaedic Association standards has declined under rising caseloads and workplace pressures. AI-powered medical scribes like Heidi AI utilise large language models to generate real-time structured documentation, potentially reducing clerical burden. However, concerns exist regarding AI accuracy. This study compares Heidi AI against conventional human dictation-transcription across efficiency, accuracy, and readability. Methods Thirty-five orthopaedic consultations were transcribed using Heidi AI and conventional human dictation-transcription by one consultant surgeon and one medical secretary. Accuracy was evaluated using a ten-category error-classification framework by a GMC-registered orthopaedic doctor. Readability was assessed using SMOG Index, Reach score, Flesch-Kincaid Grade Level, and Flesch Reading Ease. Paired t-tests compared metrics with Cohen's d effect sizes. Results Heidi demonstrated significantly fewer errors (0.29 vs 1.06 per record, p=0.0114, Cohen's d=0.68), representing 72% error reduction, and instantaneous processing versus 5.82 minutes per consultation. G2 produced significantly more readable text across all metrics with large effect sizes (all p<0.001, Cohen's d>1.10). Critically, one G2 document contained an error altering patient care; no such errors occurred with Heidi. Conclusions Heidi AI demonstrated superior accuracy and efficiency with statistically significant and clinically meaningful differences. Although G2 achieved better readability with large effect sizes, this is outweighed by Heidi AI's enhanced accuracy and elimination of critical errors, particularly given mandatory physician review. Heidi AI represents a viable solution addressing documentation burdens while maintaining superior transcription fidelity.