ReviewNPJ digital medicine2024
Responsible development of clinical speech AI: Bridging the gap between clinical research and technology.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis.Age and ageing · 2025Pooled it
- Population pharmacokinetics of imipenem in different populations for individualized dosing: a systematic review.Frontiers in pharmacology · 2025Pooled it
- Validating objective and scalable speech markers of depression across two independent psychiatric cohorts.Annals of general psychiatry · 2026Article
- Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects.Laryngoscope investigative otolaryngology · 2026Article
- Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.Neuropsychologia · 2026Article
- Review
- Prediction of Aspiration Risk by Using Vocal Biomarkers: Machine Learning Development and Validation Study.JMIR formative research · 2026Article
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Article
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- An interpretable, clinically grounded framework for digital speech biomarker development in neurodegenerative diseases.Frontiers in digital health · 2026Article
- A mechanistically informed framework of hierarchical temporal speech organization for objective differential assessment of motor speech disorders in neurodegenerative diseases.Frontiers in neuroscience · 2026Article
- Foundations for pediatric vocal biomarkers: age-aware phoneme recognition and latent-space error analysis.Frontiers in human neuroscience · 2026Article
- An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026Article
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Application of AI and digital health tools in public health management of T2DM: from mechanism prediction to personalized treatment.Frontiers in public health · 2026Review
- Exploring Acoustic Correlates of Depression and Preliminary Screening Models Using XGBoost and SHAP.Behavioral sciences (Basel, Switzerland) · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Transforming Speech-Language Pathology with AI: Opportunities, Challenges, and Ethical Guidelines.Healthcare (Basel, Switzerland) · 2025Review
- Remote Monitoring of Amyotrophic Lateral Sclerosis Using Digital Health Technologies: Shifting Toward Digitalized Care and Research?Neurology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.