Methodological quality, reproducibility, clinical deployment readiness, and generalisation strategies of machine-learning models for speech-based neurological diagnosis under data scarcity: a systematic evidence synthesis (2015–2026)
Year: 2026
Type: preprint
Study design: Systematic Review
Abstract: This study is a systematic evidence synthesis comprising a systematic meta-research review and a complementary systematic methodological review/taxonomy. The review will systematically identify and synthesize empirical studies applying machine-learning methods to speech or acoustic biomarkers for neurological diagnosis or assessment under data-scarce conditions. Two prespecified analytical outputs will be generated from the common evidence base. Paper A will evaluate methodological quality, reproducibility, validation practices, and clinical deployment readiness. Paper B will characterize and classify strategies used to improve model generalisation under data scarcity and develop a taxonomy of these strategies. The review will be conducted in accordance with PRISMA 2020 and relevant guidance for systematic review protocols and literature-search reporting.
Language: English
Topic: Voice and Speech Disorders
Subfield: Speech and Hearing
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7204666083