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.
CITATION STYLE
Šuster, S., Tulkens, S., & Daelemans, W. (2017). A Short Review of Ethical Challenges in Clinical Natural Language Processing. In EACL 2017 - Ethics in Natural Language Processing, Proceedings of the 1st ACL Workshop (pp. 80–87). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-1610
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