ReviewFrontiers in digital health2022
Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell.
Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Pooled it
- A frequency analysis of filterbank initialisation and noise augmentation for LEAF.Scientific reports · 2026Article
- Speech-based digital endpoints track ALS progression and align with standard clinical outcomes: evidence from the VRG50635 trial.Scientific reports · 2026Article
- Affective Dimensions in Maternal Voice During Child Feeding in Mothers With and Without Eating Disorder History-Findings From a Machine Learning Analysis of Speech Data.European eating disorders review : the journal of the Eating Disorders Association · 2026Article
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Research on Cancer Prediction Based on Feature Optimization and Multimodal Fusion.Health care science · 2025Article
- Speaker-independent dysarthria severity classification using self-supervised transformers and multi-task learning.PLOS digital health · 2025Article
- Multimodal Technologies for Remote Assessment of Neurological and Mental Health.Journal of speech, language, and hearing research : JSLHR · 2024Review
- Predicting age of onset and progression of disease in late-onset genetic neurodegenerative diseases: An ethics review and research agenda.European journal of human genetics : EJHG · 2024Review
- Multimodal speech biomarkers for remote monitoring of ALS disease progression.Computers in biology and medicine · 2024Article
- Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression.medRxiv : the preprint server for health sciences · 2024Article
- Remote Assessment for ALS using Multimodal Dialog Agents: Data Quality, Feasibility and Task Compliance.Interspeech · 2023Article
- Combining Multiple Multimodal Speech Features into an Interpretable Index Score for Capturing Disease Progression in Amyotrophic Lateral Sclerosis.Interspeech · 2023Article
- Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.Sensors (Basel, Switzerland) · 2023Article
- Discriminating between sick and healthy faces based on early sickness cues: an exploratory analysis of sex differences.Evolution, medicine, and public health · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
- Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, advancements in the field of artificial intelligence (AI) have impacted several areas of research and application. Besides more prominent examples like self-driving cars or media consumption algorithms, AI-based systems have further started to gain more and more popularity in the health care sector, however whilst being restrained by high requirements for accuracy, robustness, and explainability. Health-oriented AI research as a sub-field of digital health investigates a plethora of human-centered modalities. In this article, we address recent advances in the so far understudied but highly promising audio domain with a particular focus on speech data and present corresponding state-of-the-art technologies. Moreover, we give an excerpt of recent studies on the automatic audio-based detection of diseases ranging from acute and chronic respiratory diseases via psychiatric disorders to developmental disorders and neurodegenerative disorders. Our selection of presented literature shows that the recent success of deep learning methods in other fields of AI also more and more translates to the field of digital health, albeit expert-designed feature extractors and classical ML methodologies are still prominently used. Limiting factors, especially for speech-based disease detection systems, are related to the amount and diversity of available data, e. g., the number of patients and healthy controls as well as the underlying distribution of age, languages, and cultures. Finally, we contextualize and outline application scenarios of speech-based disease detection systems as supportive tools for health-care professionals under ethical consideration of privacy protection and faulty prediction.
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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.