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

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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

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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

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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

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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

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A Mission Driven Approach to Machine Learning for Healthcare Conversation

Konam S, Rao S

Journal of Commercial Biotechnology, 2021

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