CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
This work addresses the need for clinical AI systems to generate health-related explanations, though it is incremental as it builds on existing models and datasets.
The paper tackled the problem of assessing text generation models' ability to act as implicit clinical knowledge bases by generating explanations for health conditions across dimensions, showing that models perform decently but the task remains challenging with potential for further exploration.
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an associated dataset, CHARDat, consisting of explanations about 52 health conditions across three clinical dimensions. We conduct extensive experiments using BART and T5 along with data augmentation, and perform automatic, human, and qualitative analyses. We show that while our models can perform decently, CHARD is very challenging with strong potential for further exploration.