Establishing clinical interpretability of automatic speech assessments in Alzheimer's disease
Many conventional clinical trial endpoints in Alzheimer's disease are subjective, lack sensitivity and are burdensome to administer. The promise of automatic language and speech analytics has been touted as providing brief, objective, and non-invasive assessments targeting core clinical features of Alzheimer's disease. This study marks an important step in turning such promise into reality and helping advance this game-changing technology into global clinical trials.
Authors
- Iris Edda Nowenstein (ORCID: https://orcid.org/0000-0003-1945-163X)
- Gunnar Thor Örnólfsson
- Jennifer Sorinas (ORCID: https://orcid.org/0000-0002-8726-4847)
- Jelena Curcic (ORCID: https://orcid.org/0000-0001-9647-5972)
- Jessica Robin (ORCID: https://orcid.org/0000-0003-4153-2655)
- Bryndís Björk Bergþórsdóttir
- Daniel Garellick
- Kristin Hannesdottir
- Hinrik Hafsteinsson
Institutions
- Reykjavík University (IS)
- Novartis (Switzerland) (CH)
- University of Iceland (IS)
- Novartis (United States) (US)
- Cambridge Cognition (United Kingdom) (GB)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_097515
- Primary Topic
- Neurobiology of Language and Bilingualism
- Type
- article
- Field-Weighted Citation Impact
- 0.00