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Review Papers | Information Technology | United States of America | Volume 14 Issue 1, January 2025 | Rating: 6.1 / 10
A Systematic Analysis of Accuracy and Trust in Clinical Decision Making with NLP
Chandra Prakash, Musa Shaikh, Pavan Mutha, Praveen Kumar Parimi
Abstract: This systematic review examines the role of Natural Language Processing (NLP) in enhancing the efficiency and accuracy of clinical documentation. The study investigates key NLP techniques utilized in the automated generation and summarization of clinical notes. It analyzed 152 peer-reviewed articles from the past 24 months, as recent studies are crucial given the rapidly evolving landscape of machine learning, AI, and their applications in the medical field. The findings show that though NLP significantly enhances clinical documentation, increasing efficiency and accuracy through automation, there remains a concern about Trust and Transparency. This systematic review proposes novel methods to address the trust and transparency issues in clinical decision-making with NLP. This research is vital for developing NLP applications that aid clinical decision-making, reduce clinician burnout, improve patient outcomes, and support personalized healthcare. The review concludes with suggestions for future research to create adaptable and context-aware NLP models.
Keywords: Natural Language Processing (NLP), Clinical Documentation, Decision Support, Healthcare
Edition: Volume 14 Issue 1, January 2025,
Pages: 656 - 665