13 citations · 26 across the 15 of their papers we have counts for
6 papers · 1 filter
Extrinsically-Focused Evaluation of Omissions in Medical Summarization
Elliot Schumacher, Daniel Rosenthal, Dhruv Naik +4
Large language models (LLMs) have shown promise in safety-critical applications such as healthcare, yet the ability to quantify performance has lagged. An example of this challenge…
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue
Maksim Eremeev, Ilya Valmianski, Xavier Amatriain +1
Factual correctness is often the limiting factor in practical applications of natural language generation in high-stakes domains such as healthcare. An essential requirement for ma…
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
Varun Nair, Elliot Schumacher, Anitha Kannan
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference f…
Dialogue-Contextualized Re-ranking for Medical History-Taking
Jian Zhu, Ilya Valmianski, Anitha Kannan
AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extre…
CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants
Albert Yu Sun, Varun Nair, Elliot Schumacher +1
A wave of new task-based virtual assistants has been fueled by increasingly powerful large language models (LLMs), such as GPT-4 (OpenAI, 2023). A major challenge in deploying LLM-…
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
Varun Nair, Elliot Schumacher, Geoffrey Tso +1
Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these…