Read more
The evolving landscape of technology has presented numerous opportunities for addressing some of the most critical challenges in high-stakes domains such as medicine, law, and finance. These fields, where the stakes are exceptionally high, have increasingly turned to Natural Language Processing (NLP) to manage, interpret, and utilize vast amounts of unstructured linguistic data. The complexities and subtleties inherent in human language pose significant challenges in these sectors, where precision and clarity are paramount. Misinterpretation or ambiguity can lead to far-reaching consequences, making the need for advanced NLP techniques crucial.
This book aims to bridge the gap between state-of-the-art NLP technologies and their practical applications in medicine, law, and finance. By focusing on the specific challenges and advancements within these sectors, the publication intends to highlight innovative approaches, methodologies, and technologies that are shaping the future of NLP. It discusses the integration of NLP with other technological advancements, the development of new tools and techniques, and the ethical considerations involved in deploying NLP solutions in high-stakes domains.
Moreover, the book provides a platform for researchers, practitioners, and industry experts to share their experiences, insights, and research findings. Through comprehensive reviews, case studies, and empirical research, it covers a range of topics including but not limited to handling uncertainty in clinical notes, approaches for dealing with ambiguity in legal documents, sentiment analysis in financial markets, and ethical considerations in the use of NLP for sensitive data.
List of contents
Preface.- 1. Introduction to Natural Language Processing in High-Stakes Domains.- 2. NLP in Medicine: Enhancing Diagnostics and Patient Care.- 3. NLP in the Legal Domain: Ensuring Precision and Compliance.- 4. Introduction to NLP in Finance: Sentiment Analysis and Risk Management.- 5. Managing Uncertainty in NLP: Advanced Techniques and Approaches.- 6. NLP for Fraud Detection and Security in Financial Documents.- 7. Multilingual and Cross-Linguistic Challenges in NLP.- 8. NLP in Action: Case Studies from Healthcare, Finance, and Industry.- 9. Generative Large Language Models in Clinical, Legal and Financial Domains.- 10. Responsible and Ethical AI in Natural Language Processing.
About the author
Dr. Akshi Kumar is a Senior Lecturer (Associate Professor) and Director of Post-graduate Research in the Department of Computing at Goldsmiths, University of London. With over a decade of experience in academia and research, her expertise spans Natural Language Processing (NLP), AI ethics, and explainable AI. Recognized among the Top 2% highly cited scientists globally by Stanford University for four consecutive years (2021–2024), Dr. Kumar has an impressive portfolio of over 110 journal publications and 70 conference papers. Her research focuses on using AI and NLP to address societal challenges, such as online harm detection, mental and physical health interventions, and public trust in generative AI models. Dr. Kumar is a member of Steering Committee for Online Safety under the Mayor of London’s Violence Reduction Unit. In this role, she contributes to policy-forming discussions and initiatives aimed at reducing online harm and ensuring safer digital environments. She has also contributed written evidence to UK Parliament inquiries on AI's impact on public trust and digital media. A prolific author, Dr. Kumar actively engages in collaborations with global institutions and is passionate about integrating AI technologies for social good while fostering diversity and ethical innovation in computing.
Dr. Saurabh Raj Sangwan is an Assistant Professor in the School of Computer Science and Engineering, Artificial Intelligence and Machine Learning at G L Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India. He received his doctorate from Netaji Subhas University of Technology (NSUT), New Delhi in 2022. He did his bachelor’s degree in computer science and engineering from DCRUST, Murthal, Haryana, India, and obtained the M.Tech. degree in software engineering from the Department of Computer Science & Engineering, Delhi Technological University, Delhi, India, in 2018. Dr. Sangwan is also a recipient of the commendable research award from NSUT, Delhi. His works have been referred in various evidence published in the UK Parliament. His research interests include cyber informatics, online behavior, natural language processing and health informatics.
Summary
The evolving landscape of technology has presented numerous opportunities for addressing some of the most critical challenges in high-stakes domains such as medicine, law, and finance. These fields, where the stakes are exceptionally high, have increasingly turned to Natural Language Processing (NLP) to manage, interpret, and utilize vast amounts of unstructured linguistic data. The complexities and subtleties inherent in human language pose significant challenges in these sectors, where precision and clarity are paramount. Misinterpretation or ambiguity can lead to far-reaching consequences, making the need for advanced NLP techniques crucial.
This book aims to bridge the gap between state-of-the-art NLP technologies and their practical applications in medicine, law, and finance. By focusing on the specific challenges and advancements within these sectors, the publication intends to highlight innovative approaches, methodologies, and technologies that are shaping the future of NLP. It discusses the integration of NLP with other technological advancements, the development of new tools and techniques, and the ethical considerations involved in deploying NLP solutions in high-stakes domains.
Moreover, the book provides a platform for researchers, practitioners, and industry experts to share their experiences, insights, and research findings. Through comprehensive reviews, case studies, and empirical research, it covers a range of topics including but not limited to handling uncertainty in clinical notes, approaches for dealing with ambiguity in legal documents, sentiment analysis in financial markets, and ethical considerations in the use of NLP for sensitive data.
