# 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  
[Read whitepaper](/content/ai/science-ai-evaluation/index.html)

### PART II
**The Science of Confabulation Elimination Toward Hallucination-Free AI-Generated Clinical Notes**  
Michael Oberst, Davis Liang, Zack Lipton  
[Read whitepaper](/content/ai/science-confabulation-hallucination-elimination/index.html)

## External Publications

1. **Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends**  
   Sanjana Ramprasad, Elisa Ferracane, Zachary C. Lipton  
   Association for Computational Linguistics (ACL) 2024  
   [VIEW](https://aclanthology.org/2024.acl-long.677/)

2. **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](https://aclanthology.org/2021.acl-long.384/)

3. **The Mythos of Model Interpretability: In Machine Learning, the Concept of Interpretability is Both Important and Slippery**  
   Lipton ZC  
   Queue, 2018  
   [VIEW](https://arxiv.org/abs/1606.03490)

4. **Learning to Diagnose with LSTM Recurrent Neural Networks**  
   Lipton ZC, Kale DC, Elkan C, Wetzel R  
   International Conference on Learning Representations (ICLR), 2016  
   [VIEW](https://arxiv.org/abs/1511.03677)

5. **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](https://arxiv.org/abs/2308.16884)

6. **Generating More Faithful and Consistent SOAP Notes Using Attribute-Specific Parameters**  
   Ramprasad S, Ferracane E, Selvaraj SP  
   Proceedings of Machine Learning Research, 2023  
   [VIEW](https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/64d1b39ea8f777707fba6ffd/1691464606667/ID197_Research+Paper_2023.pdf)

7. **ASR Error Detection via Audio-Transcript entailment**  
   Meripo NV, Konam S  
   Interspeech, 2022  
   [VIEW](https://arxiv.org/abs/2207.10849)

8. **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](https://www.aclweb.org/anthology/2020.clinicalnlp-1.20/)

9. **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](https://arxiv.org/abs/2010.02246)

10. **Extracting Appointment Spans from Medical Conversations**  
    Meripo NV, Konam S  
    Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, 2021  
    [VIEW](https://www.aclweb.org/anthology/2021.nlpmc-1.6/)

11. **Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations**  
    Schloss BJ, Konam S  
    Proceedings of Machine Learning for Healthcare (MLHC), 2020  
    [VIEW](https://arxiv.org/abs/2007.08749)

12. **Towards Fairness in Classifying Medical Conversations into SOAP Sections**  
    Ferracane E, Konam S  
    Trustworthy AI for Healthcare Workshop, AAAI Conference on Artificial Intelligence, 2021  
    [VIEW](https://arxiv.org/abs/2012.07749)

13. **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  
    [VIEW](https://www.aclweb.org/anthology/2020.nlpmc-1.2/)

14. **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](https://arxiv.org/abs/2003.07692)

15. **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  
    [VIEW](https://arxiv.org/abs/1912.04961)

16. **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](https://arxiv.org/abs/2007.07151)

17. **A Mission Driven Approach to Machine Learning for Healthcare Conversation**  
    Konam S, Rao S  
    Journal of Commercial Biotechnology, 2021  
    [VIEW](/content/ai/publications#/index.html)