Cutting-Edge Research in AI and Healthcare | Abridge Publications

Contributions to AI research

The machine learning team at Abridge conducts cutting-edge research that powers development. The end result is a product that rapidly improves its ability to summarize and structure medical conversations for clinicians and patients alike.

Whitepapers

PART I

Pioneering the Science of Evaluation for Gen AI in Healthcare
Michael Oberst, Davis Liang, Zack Lipton
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PART II

The Science of Confabulation Elimination Toward Hallucination-Free AI-Generated Clinical Notes
Michael Oberst, Davis Liang, Zack Lipton
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External Publications

Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends
Sanjana Ramprasad, Elisa Ferracane, Zachary C. Lipton
Association for Computational Linguistics (ACL) 2024
VIEW
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Krishna K, Khosla S, Bigham J, Lipton ZC
Association for Computational Linguistics (ACL) 2021
VIEW
The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability is Both Important and Slippery
Lipton ZC
Queue, 2018
VIEW
Learning to Diagnose with LSTM Recurrent Neural Networks
Lipton ZC, Kale DC, Elkan C, Wetzel R
International Conference on Learning Representations (ICLR), 2016
VIEW
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
Bandarkar L, Liang D, Muller B, Artetxe M, Shukla SN, Husa D, Goyal N, Krishnan A, Zettlemoyer L, Khabsa M
Association for Computational Linguistics (ACL), 2024
VIEW
Generating More Faithful and Consistent SOAP Notes Using Attribute-Specific Parameters
Ramprasad S, Ferracane E, Selvaraj SP
Proceedings of Machine Learning Research, 2023
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ASR Error Detection via Audio-Transcript entailment
Meripo NV, Konam S
Interspeech, 2022
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Weakly Supervised Medication Regimen Extraction from Medical Conversations
Patel D, Konam S, Prabhakar S
Proceedings of the 3rd Clinical Natural Language Processing Workshop, 2020
VIEW
MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge
Khosla S, Vashishth S, Lehman JF, Rose C
Proceedings of The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
VIEW
Extracting Appointment Spans from Medical Conversations
Meripo NV, Konam S
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, 2021
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Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations
Schloss BJ, Konam S
Proceedings of Machine Learning for Healthcare (MLHC), 2020
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Towards Fairness in Classifying Medical Conversations into SOAP Sections
Ferracane E, Konam S
Trustworthy AI for Healthcare Workshop, AAAI Conference on Artificial Intelligence, 2021
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Towards Understanding ASR Error Correction for Medical Conversations
Mani A, Palaskar S, Konam S
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations, 2020
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ASR Error Correction and Domain Adaptation Using Machine Translation
Mani A, Palaskar S, Meripo NV, Konam S, Metze F
ICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2020
VIEW
Medication Regimen Extraction From Medical Conversations
Selvaraj SP, Konam S
Proceedings of the International Workshop on Health Intelligence (W3PHIAI) of the 34th AAAI Conference on Artificial Intelligence, 2020
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Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances
Krishna K, Pavel A, Schloss BJ, Bigham J, Lipton ZC
Proceedings of the International Workshop on Health Intelligence (W3PHIAI) of the 34th AAAI Conference on Artificial Intelligence, 2020
VIEW
A Mission Driven Approach to Machine Learning for Healthcare Conversation
Konam S, Rao S
Journal of Commercial Biotechnology, 2021
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