Rudzicz, Frank

Linguistic Features Identify Alzheimer’s Disease in Narrative Speech

Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease

Dementia and Cognitive Impairment ResearchNeurobiology of Language and Bilingualism

Cited by 797

The TORGO database of acoustic and articulatory speech from speakers with dysarthria

Rudzicz, F., Namasivayam, A. K., & Wolff, T. (2012) · Language Resources and Evaluation

Voice and Speech DisordersSpeech Recognition and Synthesis

Cited by 425

Artificial intelligence and the implementation challenge

Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019) · Journal of Medical Internet Research

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 389

Speech Interaction with Personal Assistive Robots Supporting Aging at Home for Individuals with Alzheimer’s Disease

Rudzicz, F., Wang, R., Begum, M., & Mihailidis, A. (2015) · ACM Transactions on Accessible Computing

Social Robot Interaction and HRISpeech and dialogue systems

Cited by 83

Adapting acoustic and lexical models to dysarthric speech

Mengistu, K. T., & Rudzicz, F. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisVoice and Speech Disorders

Cited by 82

Using linguistic features longitudinally to predict clinical scores for Alzheimer's disease and related dementias

Yancheva, M., Fraser, K., & Rudzicz, F. (2015) · Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 76

Speech Recognition in Alzheimer’s Disease and in its Assessment

Zhou, L., Fraser, K. C., & Rudzicz, F. (2016) · Interspeech 2016

Dementia and Cognitive Impairment ResearchNeurobiology of Language and Bilingualism

Cited by 62

Using text and acoustic features to diagnose progressive aphasia and its subtypes

Fraser, K. C., Rudzicz, F., & Rochon, E. (2013) · Interspeech 2013

Dementia and Cognitive Impairment ResearchArtificial Intelligence in Healthcare

Cited by 61

A Conversational Robot for Older Adults with Alzheimer's Disease

Pou-Prom, C., Raimondo, S., & Rudzicz, F. (2020) · ACM Transactions on Human-Robot Interaction

Social Robot Interaction and HRISpeech and dialogue systems

Cited by 59

Vector-space topic models for detecting Alzheimer's disease

Yancheva, M., & Rudzicz, F. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dementia and Cognitive Impairment ResearchNeurobiology of Language and Bilingualism

Cited by 57

Comparing Humans and Automatic Speech Recognition Systems in Recognizing Dysarthric Speech

Mengistu, K. T., & Rudzicz, F. (2011) · Lecture Notes in Computer Science

Speech Recognition and SynthesisVoice and Speech Disorders

Cited by 55

Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care

Seth, P., Hueppchen, N., Miller, S. D., Rudzicz, F., Ding, J., Parakh, K., & Record, J. D. (2023) · JMIR Medical Education

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 52

Identifying and Avoiding Confusion in Dialogue with People with Alzheimer's Disease

Chinaei, H., Currie, L. C., Danks, A., Lin, H., Mehta, T., & Rudzicz, F. (2017) · Computational Linguistics

Speech and dialogue systemsDementia and Cognitive Impairment Research

Cited by 30

Talk2Me: Automated linguistic data collection for personal assessment

Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE

Dementia and Cognitive Impairment ResearchMental Health via Writing

Cited by 23

Deep Learning Model for Automated Trainee Assessment During High-Fidelity Simulation

Siddiqui, A., Zhao, Z., Pan, C., Rudzicz, F., & Everett, T. (2023) · Academic Medicine

Simulation-Based Education in HealthcareSurgical Simulation and Training

Cited by 15

Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report

Nunes, A., Pavlova, B., Cunningham, J. E. A., Nuñez, J.-J., Quilty, L. C., Foster, J. A., Harkness, K. L., Ho, K., Lam, R. W., Li, Q. S., et al. (2024) · Journal of Affective Disorders

Mental Health Research TopicsTreatment of Major Depression

Cited by 5

Open technical problems in open-weight ai model risk management

Casper, S., O'Brien, K., Longpre, S., Seger, E., Klyman, K., Bommasani, R., Nrusimha, A., Shumailov, I., Mindermann, S., Basart, S., et al. (2025) · SSRN Electronic Journal

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 3

Trustworthy Medical Question Answering: An Evaluation-Centric Survey

Wang, Y., Wang, B., Mercer, R. E., Rudzicz, F., Roy, S. S., Ren, P., Chen, Z., & Wang, X. (2025) · arXiv

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 0