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Found 10 papers, 2 papers with code
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Summarized conversations are used to facilitate patient hand-offs between physicians, and as part of providing care in the future.
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue.
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.
Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge.
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge.
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them.
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred.
Domain-Relevant Embeddings for Medical Question Similarity
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique.
Open Set Medical Diagnosis
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i. e. that models will only encounter conditions on which they have been trained.
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue
Only 12% of our discriminative approach's responses are worse than the doctor's response in the same conversational context, compared to 18% for the generative model.
