A Short Review of Ethical Challenges in Clinical Natural Language Processing
It tackles ethical challenges in clinical NLP for researchers and practitioners, but is incremental as it reviews existing issues without new solutions.
The paper addresses the slow progress in clinical NLP due to strict data access policies, discussing privacy concerns, suggesting alternative data sources, and highlighting biases that can harm research validity and social applications.
Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.