Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems

Clinical Snapshot

Key Findings

• P Value: Not applicable - descriptive review

• Effect Size: Wide variability: 25-100% (image-based) and 72-99.7% (sensor-based)

• Primary Outcome: System accuracy for sign language recognition

• Nnt Or Sensitivity: Sensitivity ranges from 25-100% depending on system type and implementation

• Confidence Interval: Not reported

Clinical Application

Currently limited - all systems in development stage with significant technical and implementation barriers Relevant for Australian healthcare given similar communication challenges, though would require adaptation for Auslan (Australian Sign Language) and integration with existing telehealth infrastructure Deaf patients using sign language in healthcare settings, particularly emergency departments and general hospital care

Abstract

BACKGROUND: Deaf individuals often face communication challenges when interacting with those who can hear. Within health care settings, these challenges may pose risks to their safety, potentially resulting in misdiagnoses, treatment errors, and decreased quality of care. OBJECTIVE: This study aims to systematically review the evidence on communication systems reported in the literature that use human-computer interaction techniques to support communication between deaf individuals who use sign language and hearing health professionals in health care settings. The review focuses on systems that are either currently in use or proposed for use in health care and that have been tested using human participants or videos of human users. METHODS: A comprehensive search was performed via MEDLINE, Web of Science, ACM, IEEE Xplore, Scopus, and Google Scholar in March 2025. The inclusion criteria comprised studies developing a sign language recognition system within a health care context and testing with human users. Eligible studies underwent screening by 2 independent investigators (LRV and LMMSR or LFRdO and GTdSS), with any disagreements resolved by a senior researcher (MSM). RESULTS: The search retrieved 21,778 publications, and screening of reference lists identified 2 additional studies, resulting in a total of 23 studies meeting the eligibility criteria. Most systems (15/23, 65.2%) were image-based, while 34.8% (8/23) relied on sensors (glove-based or depth-sensing). Applications varied across health care settings, including general hospital care (10/23, 43.5%), emergencies (8/23, 34.8%), and primary care (4/23, 17.4%). All systems were in the development and testing stage, with no data on security and psychological impacts. Accuracy ranged from 25% to 100% for image-based and 72% to 99.7% for sensor-based systems. Bidirectionality and facial expression recognition, crucial for effective communication, were largely overlooked. CONCLUSIONS: Image-based systems were more common than sensor-based ones, though both showed wide variability in accuracy in recognizing and interpreting signs. Most systems failed to address critical aspects such as bidirectional communication and the recognition of facial expressions, essential for effective communication. None fully addresses the requirements for integration into health care settings. These findings highlight the need for further research on implementation, usability, and impact on the quality of care for deaf patients.

References

1. 1.Marcolino, M. S., Oliveira, L. F. R., Valle, L. R., Rosa, L. M. M. S., Sanches, G. T. S., Santos, N. S., Costa, M. R., Bernardino, E. L. A., Cordeiro, R. A. A., Prates, R. O., Reis, Z. S. N., & Campos, M. F. M. (2026). Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems. Journal of Medical Internet Research, 28(4), e70417. https://doi.org/10.2196/70417