01
AI-supported clinical reasoning
Evaluate how language models interpret audiology histories, audiograms, symptoms, urgency cues, and management options.
Research theme
AI-Enabled Hearing Care
We study how AI systems can support hearing-care education, assessment, counseling, triage, and clinical decision support while preserving expert oversight.
Positioning
The goal is not to replace audiologists. The goal is to evaluate where AI can make hearing care more accessible, understandable, and evidence-informed.
Active
02
Patient-facing explanations
Study whether AI-generated hearing-care information is accurate, understandable, actionable, and appropriately reassuring.
03
Audiology education and workforce support
Explore how generative AI can broaden access to training resources, continuing education, and clinical learning materials.
04
Model evaluation and safety
Develop review workflows that compare AI outputs against expert judgment, missing information, and potential safety risks.
Evaluation pathway
From measurement to AI review to clinical translation.
Measure
Collect structured hearing, speech-in-noise, questionnaire, and clinical-context data.
Model
Generate or evaluate AI outputs with clear prompts, version records, and human review criteria.
Translate
Study whether outputs improve education, access, communication, or care-pathway decisions.
Publications
Recent papers and commentaries on AI chatbots, audiology education, and hearing-care applications.
Open
Assessment tools
Digital tools that create the measurement layer for AI-enabled hearing-care workflows.
Open
Hearing healthcare in China
Health-services work on audiology roles, access, workforce, and professional identity.
Open