This paper examines the role of NLP in these high-stakes environments, highlighting key technologies such as transformers, named entity recognition (NER), and domain-specific transfer learning and addresses the unique challenges NLP faces in these contexts.
A comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT demonstrates that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.
P. Kalaiselvi· International Journal of Eme...· 0 citations
An engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation is introduced.
Text content is the dominant factor in annotation decisions, far outweighing annotator demographics, and that content-focused SHAP explanations are more effective than demographic persona prompting for guiding LLM annotations, showing that explainability methods can improve both the reliability and the transparency of NLP systems.
In recent years, natural language processing has become an important tool in healthcare for extracting useful information from unstructured clinical text such as electronic health records, physician notes, and medical literature. Deep learning has significantly improved the performance of NLP systems, enabling stronger results in tasks such as disease prediction, clinical decision support, and patient risk assessment. However, healthcare NLP still faces major challenges in real-world deployment. Clinical text is often noisy, fragmented, and inconsistent, which can reduce model reliability. In addition, deep learning models lack transparency, which limits their adoption by clinicians who require explainable outputs for clinical decision-making. Privacy and security also remain major barriers because patient data is highly sensitive and subject to strict legal and ethical requirements. Bias in training data can further lead to uneven performance across patient populations. This paper combines a literature review with a healthcare-oriented case study to examine these issues in real-world settings. The findings show that although deep learning offers strong potential for healthcare analytics, progress depends on solving problems related to data quality, interpretability, privacy, and domain adaptation.
Madhurima Kommuru, Swathi Thatraju, Appala Nooka Kumar Doodala· International Journal of Mac...· 0 citations
Legal epidemiology — the study of how laws influence health outcomes — is an emerging field with potential to inform policy and improve public health. Shortages of specialists who can commit the extensive time required for these analyses has hindered progress in this area. Features of laws make them ideal candidates for artificial intelligence and natural language processing (AI/NLP) to address these constraints: standardized format, defined terms, and common terminology. However, there are concerns regarding valid AI/NLP application, especially given current underreporting of legal research in published policy evaluation studies. Drawing on lessons learned from case deployments and recent literature, we review opportunities and challenges for methods using AI/NLP in scientific legal research: assessing the scope of legal documents and data, identifying and collecting primary legal data, developing and applying coding schemes, and implementing quality controls to ensure the validity of produced legal datasets. We highlight methodologic research areas and innovation in the application of AI/NLP for scientific studies regarding effects of law on health and indicate methodologic reporting elements that will be essential for the field’s innovation. This review provides a method-focused research agenda for AI/NLP in scientific legal epidemiology studies that might accelerate the field’s growth and effects on evidence-based policy.
Regen Weber-Fares, F. Cochlin, Snigdha R Peddireddy et al.· Journal of Law, Medicine & E...· 0 citations
A thorough review of the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations are reviewed to suggest a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices.
Noah Wright· International Journal of Mod...· 0 citations
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