Research Publications at Curai
Blog Main PageLast updated: March 14, 2024
Curai’s commitment to AI is to impact primary healthcare and advance research/science meaningfully
Since Curai was founded in 2017, we have published 21 peer-reviewed papers and received about 325 citations, including citations from other leading healthcare organizations. This commitment to transparency in AI research and the pursuit of academic, peer-reviewed publications is central to our mission.
Extrinsically-Focused Evaluation of Omissions in Medical Summarization E Schumacher, D Rosenthal, V Nair, L Price, G Tso, A Kannan Arxiv, 2023
CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants A Sun, V Nair, E Schumacher, A Kannan NAACL, 2024
Extrinsically-Focused Evaluation of Omissions in Medical Summarization E Schumacher, D Rosenthal, V Nair, L Price, G Tso, A Kannan Arxiv, 2023
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents V Nair, E Schumacher, G Tso, A Kannan Arxiv, 2023
Injecting knowledge into language generation — A case study in auto-charting after-visit care instructions from medical dialogue M Ereemev, I Valmianski, X Amatrian, A Kannan ACL, 2023
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models V Nair, E Schumacher, A Kannan ACL Clinical NLP Workshop, 2023
Dialog-Contextualized Re-ranking for Medical History Taking J Zhu, I Valmianski, A Kannan Machine Learning for Healthcare, 2023
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach M Wang, I Valmianski, X Amatrian, A Kannan Machine Learning for Healthcare, 2023
OSLAT: Open Set Label Attention Transformer for medical entity span extraction R Li, I Valmianski, L Deng, X Amatrian, A Kannan Machine Learning for Health (ML4H), 2022
MEDCOD: A medically-accurate, Emotive, Diverse, and Controllable Dialog System R Compton, I Valmianski, L Deng, C Huang, N Katariya, X Amatrian, A Kannan Machine Learning for Health (ML4H), 2021
Adding more data does not always help: A study in medical conversation summarization with PEGASUS V Nair, N Katariya, I Valmianski, X Amatrian, A Kannan Machine Learning for Health (ML4H), 2021
Medically-aware GPT-3 as a data generator for medical dialog summarization B Chintagunta, N Katariya, X Amatrian, A Kannan (Best Paper award at) NAACL-Workshop on NLP for Medical Conversations, 2021 and Machine Learning for Healthcare (MLHC) 2021
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions A. Mottaghi, P. Sarma, X. Amatriain, S. Yeung, A. Kannan NeurIPS — Machine Learning for HealthCare, 2020
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures A Joshi, N Katariya, X Amatrian, A Kannan EMNLP — Findings, 2020
COVID-19 in differential diagnosis of online symptom assessments A Kannan, R Chen, V Venkataraman, G Tso, X Amatriain NeurIPS —ML4H, 2020
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs C McCreery, N Katariya, A Kannan, M Chablani, X Amatriain KDD, 2020
Open Set Medical Diagnosis V Prabhu, A Kannan, G Tso, N Katariya, M Chablani, D Sontag, X Amatriain NeurIPS Machine Learning for Health (ML4H), 2019 ACM Conference on Health, Inference, and Learning (ACM- CHIL), 2020
The accuracy vs. coverage trade-off in patient-facing diagnosis models A Kannan, J Fries, E Kramer, J Chen, N Shah, X Amatriain AMIA Informatics Summit, 2020
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue S Shleifer, M Chablani, N Katariya, A Kannan, X Amatriain NeurIPS Machine Learning for Health (ML4H), 2019
Domain-Relevant Embeddings for Medical Question Similarity C McCreery, N Katariya, A Kannan, M Chablani, X Amatriain NeurIPS Machine Learning for Health (ML4H), 2019
Prototypical Clustering Networks for Dermatological Disease Diagnosis V Prabhu, A Kannan, M Ravuri, M Chablani, D Sontag, X Amatriain NeurIPS Machine Learning for Health (ML4H), 2018 Machine Learning for Healthcare (MLHC), 2019
Learning from the experts: From expert systems to machine learned diagnosis models M. Ravuri, A. Kannan, G. Tso, X. Amatriain Machine Learning for Healthcare (MLHC), 2018