# A Short Review of Ethical Challenges in Clinical Natural Language Processing

arXiv: https://arxiv.org/abs/1703.10090 · HTML: https://mlsanity.com/paper/1703.10090/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 14 of 14)

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- [FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning](https://mlsanity.com/paper/2310.16538/) (2023)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [Resources for Turkish Natural Language Processing: A critical survey](https://mlsanity.com/paper/2204.05042/) (2022)
- [Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words](https://mlsanity.com/paper/2209.06453/) (2022)
- [Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings](https://mlsanity.com/paper/2501.18998/) (2025)
- [Use of Formal Ethical Reviews in NLP Literature: Historical Trends and Current Practices](https://mlsanity.com/paper/2106.01105/) (2021)
- [Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Data](https://mlsanity.com/paper/2208.06216/) (2022)
- [MedOrchestra: A Hybrid Cloud-Local LLM Approach for Clinical Data Interpretation](https://mlsanity.com/paper/2505.23806/) (2025)
- [HealthcareNLP: where are we and what is next?](https://mlsanity.com/paper/2512.08617/) (2025)

## Abstract (arXiv)

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.