Integration of AI-generated clinic letters in complex paediatric neurosurgery outpatient settings

Elmolla, M, Khanom, AA, Ali, AMS ORCID: 0000-0001-9437-4262, Duncan, C, Hennedige, A and Vakharia, VN (2026) Integration of AI-generated clinic letters in complex paediatric neurosurgery outpatient settings Child S Nervous System, 42 (1). 80-. ISSN 0256-7040, 1433-0350

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Abstract

Purpose: Dictation of outpatient clinic letters can result in increased workload for clinicians. The use of generative, natural language processing artificial intelligence (AI) software could be used to supplant dictation and typing, alleviating the clinician’s workload. Therefore, our objective was to validate the use of AI software, Lyrebird AI (Lyrebird Health, Ltd.) to create accurate and readable clinic letters, in the context of a single clinician general paediatric neurosurgery clinic and a multi-disciplinary craniofacial clinic. Methods: Twenty consultations were included, wherein a microphone was used to record the entire consultation. For each consultation, two letters were generated independently: (1) Lyrebird AI letter automatically generated at the end of the recording and (2) human clinician manual dictation in the usual manner. The letters were compared using objective readability metrics and a subjective rating by an independent blinded clinician for clinical accuracy. Results: AI-generated clinic letters significantly improved readability compared to clinician-dictated letters, when using the Flesch–Kincaid Grade Level (median 10.3, IQR 0.75), (median 10.9, IQR 1.15) (z = 2.73, p < 0.05), and SMOG Index (median 11.7, IQR 1.25) (median 13.1, IQR 0.9) (z = − 3.36, p < 0.001) metrics. An independent blinded clinician subjectively chose the AI-generated letters for overall preference in 75% of cases. Conclusions: Lyrebird AI–generated clinic letters increased readability whilst maintaining clinical accuracy. Future work should focus on time, effort, and cost saving analysis. Presently, the findings of this study provide validation of Lyrebird AI for complex, multi-stakeholder clinical settings.

Item Type:	Article
Uncontrolled Keywords:	Humans, Neurosurgical Procedures, Pediatrics, Neurosurgery, Artificial Intelligence, Natural Language Processing, Child, Ambulatory Care Facilities, Female, Male
Divisions:	Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Life Courses & Medical Sciences Faculty of Health & Life Sciences > Inst. Life Courses & Medical Sciences > School of Medicine
Depositing User:	Symplectic Admin
Date Deposited:	24 Feb 2026 16:24
Last Modified:	13 Mar 2026 10:26
DOI:	10.1007/s00381-026-07171-6
Open Access URL:	https://doi.org/10.1007/s00381-026-07171-6
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3197188
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