Machine learning brings understanding and follow-through to medical conversations.
a·bridge
/əˈbɹɪdʒ/
verb: shorten (a piece of writing) without losing the sense.
Our industry-leading research powers automated solutions for thousands of clinicians today.
What We’re Building
AI that summarizes medical conversations and extracts structured data for coding, billing, and risk assessment.
Rigorously tested modular pipelines that make it easy to customize the AI to your needs.
Proprietary and de-identified dataset of 1.5M+ medical encounters.
More peer-reviewed publications than any other entity in the field of medical conversation understanding.
We are a diverse team of experts in machine learning, including Carnegie Mellon University professors and their respective labs.
Benjamin Schloss, PhD
Abridge - ML Research
Elisa Ferracane, PhD
Abridge - ML Research
Jose D. Posada, PhD
Abridge - ML Research
Kundan Krishna
Carnegie Mellon University - PhD Student
Nathan Price
Abridge - ML Research
Prabhakar Sai
Abridge - ML Research
Shruti Palaskar
Carnegie Mellon University - PhD Student
Zack Lipton, PhD
Abridge - Chief Scientific Officer Carnegie Mellon University - Assistant Professor
Contributions to Research
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ASR Error Detection via Audio-Transcript entailment
Interspeech 2022 Special Session, Speech and Language in Health: from Remote Monitoring to Medical Conversations
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021
Weakly Supervised Medication Regimen Extraction from Medical Conversations
3rd Clinical Natural Language Processing Workshop, The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations
Proceedings of Machine Learning Research, Machine Learning for Healthcare (MLHC) 2020
ASR Error Correction and Domain Adaptation Using Machine Translation
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
Ready to get more from your conversations?
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