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Verification of an AI-based app enabling instant, secure communication across language barriers in health care

Reference number
Coordinator Mabel AI AB - Sahlgrenska Science Park
Funding from Vinnova SEK 300 000
Project duration November 2022 - September 2023
Status Completed
Venture Innovative Startups
Call Innovative Impact Startups autumn 2022

Important results from the project

The goal of the project was to develop and validate a MVP of an AI-based translation app for medical conversations. The unique aspect of our solution is the focus on privacy, which is of paramount importance in medicine. We conduct all our operations on the mobile device without the use of internet. This guarantees that the medical conversations stay private. During the project period, we successfully created an app that can be used for validation, and created significantly improved models for English and Ukrainian, enriched with medical data.

Expected long term effects

Improved Word-Error-Rate for speech-to-text of Ukrainian from 13% to 3.2%, by using a different network architecture, fine tuning to the medical domain, and using our own implementation of beam search. Filtered out low quality translation data, Russian words, profanities, and mismatched translations. With the help of LLMs and doctors, we collected simulated medical conversations, which were used to evaluate the new translation models. We achieved an accuracy improvement of 64% over existing English-Ukrainian models! We will make our Eng-Ukr model open-source!

Approach and implementation

Any AI system is as good as the training data. We devoted much time to gathering and cleaning data, using both manual and automated approaches. We identified a bias in the open source data, where about 80% of the voices were of men aged 20-40, and hired a female Ukrainian refugee to evaluate the performance of our models on voices outside this demographic. She identified a discrepancy between the reported accuracy of the existing models and her voice, and detected Russian words in the translation. We trained new models with cleaned and augmented data, significantly improving our accuracy.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 16 November 2023

Reference number 2022-02293