Research
Trustworthy AI for human work.
I study how AI-assisted systems can be evaluated for reliability, especially when speech, generated documentation, and human decision-making meet.
Research statement
Reliability is part of the interface.
AI systems used for documentation should do more than generate fluent text. They should help people recognize uncertainty, potential errors, and unsupported content without adding unnecessary friction to the work.
My interests span trustworthy AI, medical documentation, speech and language processing, DSP, and human-centered evaluation. The common question is simple: how do we know a system is helping, and how do we make its failure modes visible?
Current project
AI-assisted clinical documentation
This ongoing project investigates an end-to-end prototype that combines automatic speech recognition, transcript processing, medical-document generation, and validation mechanisms intended to flag potential errors or unsupported outputs.
My role
Prototype, validation, and study design.
- Developing components of the end-to-end prototype and validation layer.
- Investigating ways to surface potential errors and unsupported outputs.
- Designing a user-study and data-collection workflow around human interaction and documentation impact.
- Preparing ongoing work for a future research manuscript without representing it as published or accepted.
Research questions
What needs to be measured?
- Which generated statements are unsupported by the available source context?
- How should a validation layer communicate risk without overwhelming the user?
- How does validation affect review behavior, documentation quality, and time?
- Where do automated metrics stop being useful, and where is human evaluation essential?
Evaluation approach
Separate system quality from human impact.
The evaluation plan distinguishes component-level behavior from end-to-end use. Prototype testing examines transcription, generation, and validation behavior; the planned user workflow evaluates how people interact with the system and how it affects documentation work.
Boundaries
What is intentionally not shown.
This public case study contains no protected health information, private datasets, confidential implementation details, or clinical performance claims. Adviser, laboratory, poster, report, and manuscript links will be added only after they are confirmed and public.