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General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports
F1 score for extraction was 97% for RadLing-based system and 78% for GPT-4 system.
shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation
Instruction-tuned generative Large language models (LLMs) like ChatGPT and Bloomz possess excellent generalization abilities, but they face limitations in understanding radiology reports, particularly in the task of generating the IMPRESSIONS section from the FINDINGS section.
RadLing: Towards Efficient Radiology Report Understanding
Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus.
Toward the Engineering of Virtuous Machines
We begin to address this by presenting an embryonic formalization of a key part of any virtue-ethics theory: namely, the learning of virtue by a focus on exemplars of moral virtue.
One Formalization of Virtue Ethics via Learning
Given that there exist many different formal and precise treatments of deontologi- cal and consequentialist ethics, we turn to virtue ethics and consider what could be a formalization of virtue ethics that makes it amenable to automation.
