Research Publications at Curai
Last updated: Aug 15, 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 23 peer-reviewed papers and received over 460 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
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
V Nair, E Schumacher, G Tso, A Kannan
NAACL, Clinical NLP Workshop 2024
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
