Responsible development of clinical speech AI: Bridging the gap between clinical research and technology
Open accessAbstract
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.
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Clinical AI speech and language diagnostics can be too high stakes given the variable reliability of clinician judgment.
“Clinical AI test sets that include typical speakers would be most useful for diagnostic classification of clinical speakers versus typical speakers, but we and others contend clinical AI speech and language diagnostics can be too high stakes given the variable reliability of clinician judgment (Berisha & Liss, 2024).”
From Equipping Speech-Language Clinicians for the Critical Appraisal of an Artificial Intelligence–Driven, Evidence-Based Future (2025) · cites this paper as Berisha & Liss, 2024
The Introduction cautions that stand-alone speech-based models are unlikely to achieve high diagnostic accuracy for some conditions and warns against premature deployment. The “Diagnostic assistance” section reports variable reliability in clinician-dependent assessments, but presents objective speech measures as a way to improve diagnosis; it does not say that clinician variability makes AI diagnostics too high stakes. (LLM, conf=0.82)
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Remote Monitoring of Amyotrophic Lateral Sclerosis Using Digital Health Technologies
van Unnik, J. W. J., Ing, L., Santos, M. O., McDermott, C. J., de Carvalho, M. A., & van Eijk, R. P. A. (2025) · Neurology · doi:10.1212/wnl.0000000000213738 · Cited by 12 · see citing works
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Self-citationResearch Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials
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ChatGPT y la redacción académica en universitarios
Paredes, S. A. R., & Ledesma-Pérez, F. (2025) · Edutec Revista Electrónica de Tecnología Educativa · doi:10.21556/edutec.2025.92.3431 · Cited by 4 · see citing works
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Generative Artificial Intelligence and Large Language Models in Clinical Oncology
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Automated Extraction of Spatio-Semantic Graphs for Identifying Cognitive Impairment
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Multimorbidity measurement strategies for predicting hospital visits
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AI-Enhanced Voice Analysis for Neurological Diseases
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Cognitive data harmonization in the ADRC Network and beyond—Past, present, and future
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An AI-Powered Voice Assistant for Health and Wellness Monitoring Using CNN-BILSTMAttention
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- Authors
- Berisha, V., & Liss, J. M.
- Affiliations
- Arizona State University (US)
- Corresponding author
- Berisha, Visar
- Venue
- npj Digital Medicine
- Published
- 2024-08-09
- Volume
- 7
- Issue
- 1
- Pages
- 208-208
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11316053
- OpenAlex
- W4401450033
- Cited by
- 41
- References
- 98
- Citations by year
- 2026: 26 · 2025: 15
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- The JTMF Foundation is the John and Tami Marick Family Foundation
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- overfitting, model generalization, speech science, clinical research, speech analytics, explainable AI, model validation, responsible AI, interdisciplinary research, speech disorders
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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