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Found 8 papers, 6 papers with code
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
But how factual are these summaries in a high-stakes domain like medicine?
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness.
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting.
Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset
Automatic text simplification can help patients to better understand their own clinical notes.
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practices of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
We test the generalizability of three de-identification methods across languages and domains.
Identifying Unclear Questions in Community Question Answering Websites
Thousands of complex natural language questions are submitted to community question answering websites on a daily basis, rendering them as one of the most important information sources these days.
