Transparency in Text
Madhan Veeramani, P. Karthick, S. Venkateswaran, B. Sriman, Shaik Thasleem Bhanu, V. Susheela Devi · 2025
This paper ventures into the realm of healthcare natural language processing (NLP) and underscores the critical role of explainability in enhancing trust and understanding. As NLP technologies revolutionize healthcare practices, from clinical documentation to patient interaction analysis, the demand for transparent and interpretable NLP models becomes paramount. By leveraging recent strides in explainable AI methodologies, we delve into the inner workings of NLP algorithms, demystifying their decision-making processes within the healthcare context. Through an exhaustive examination of explainability techniques tailored for healthcare NLP tasks, we showcase their ability to illuminate model predictions, identify biases, and enhance the interpretability of clinical insights. Real-world case studies and examples demonstrate how explainable AI fosters clinician comprehension, regulatory compliance, and patient-centered care. By addressing challenges and advocating for the integration of interpretable models in healthcare NLP pipelines, we aim to advance the adoption of transparent and accountable AI-driven linguistic technologies ultimately improving healthcare delivery and patient outcomes.