Aug 2026· Genetics and Molecular Research· 0 citations· 15 references
Abstract
Natural language processing (NLP) has emerged as a key focus of AI research for the analysis, interpretation, extraction, summarisation, and generation of human language. The vast amount of unstructured textual data in scientific research, electronic health records, clinical notes, radiology reports, public health documents, and digital health platforms has driven the demand for sophisticated computational tools and techniques capable of extracting structured and actionable knowledge from language. NLP has been greatly advanced by deep learning, which allows for automatic representation learning, understanding context, modeling sequences, and generating large amounts of language by means of structures like CNN, RNN, LSTM, GRU, attention mechanisms, transformers, and large language models. This review aims to present a detailed overview of deep learning-based NLP models, methods, applications, challenges, and future directions, focusing on biomedical informatics, clinical text mining, digital health and biomathematical relevance. It has numerous applications such as biomedical literature mining, named entity recognition, relation extraction, clinical decision support, pharmacovigilance, radiology report generation, public health surveillance, and construction of knowledge graph. The specific focus lies in the application of NLP to identify biological entities, clinical variables and quantitative evidence that can be used to support biomathematical modeling. There are several current challenges such as domain shift, privacy, hallucination, bias, interpretability, and reproducibility. The success of future progress relies on reliable, comprehensible, domain specific and clinically verified NLP systems.
Abstract--Natural Language Processing (NLP) has emerged as a major branch of Artificial Intelligence (AI) that allows computers to effectively understand, interpret, and generate human language. The recent advances in machine learning, deep learning and transformer-based architectures have considerably improved the performance of NLP systems on a wide range of applications. This paper presents a comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT. It covers the major methodologies including text preprocessing, feature representation, machine learning, deep learning and transformer-based language modelling. Moreover, the study elaborates on the use of NLP in healthcare, education, business, finance, customer service, social media, and intelligent communication and highlights its role in enhancing automation, decision-making, and human–computer interaction. In addition, the paper discusses the major challenges faced by current NLP systems, including language ambiguity, multilingual processing, computational complexity, model bias, privacy, and explainability. Finally, future research directions, including lightweight language models, multilingual NLP, explainable AI, and multimodal intelligence, are presented. The findings demonstrate 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
Natural Language Processing (NLP) has become a cornerstone of artificial intelligence, enabling machines to process, understand, and generate human language. With the increasing adoption of machine learning, deep learning, and large-scale transformer models, NLP has made significant progress in the past decade. Modern NLP systems are used in applications ranging from machine translation, sentiment analysis, chatbots, and automated summarization to knowledge extraction and conversational agents. Transformer-based architectures and large language models (LLMs) have drastically improved context understanding, semantic representation, and generation quality. This paper provides a comprehensive survey of NLP, discussing its components, historical evolution, applications, datasets, evaluation metrics, recent advancements, and challenges. A detailed literature review based on studies from 2022–2026 is presented, highlighting the role of transformer models, deep learning architectures, and emerging trends such as multi-lingual NLP, domain-specific models, and ethical considerations. Finally, the paper explores future research directions to address low-resource languages, model fairness, and real-world applicability.
Ankita Vijay Shinde, Sunil Tanaji Salunkhe,, Bharati Bhaskar Khandagale· International Journal of Adv...· 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.
Natural language processing (NLP) technology is the key driving force to unlock the value of massive, unstructured medical text data and promote the development of smart medicine. This paper aims to systematically review the application of NLP in the field of health care, focusing on how “medical text mining” drives “knowledge discovery” and ultimately serves “clinical decision support”. Firstly, this paper reviews the evolution of technology from the early rule method to the current pre training language model. Then, the core technologies such as medical information extraction, knowledge map construction, text classification and generation and their application in typical scenarios such as electronic medical record analysis, auxiliary diagnosis, prognosis prediction, and patient management are reviewed. Through the induction and comparison of existing studies, this paper summarizes the main challenges currently facing, including medical data privacy and labeling problems, interpretability and clinical credibility barriers of the model, and systemic barriers to multimodal fusion. Finally, this paper looks forward to the future research directions, such as the development of interpretable AI and the construction of NLP system for real-world evidence, in order to promote the transformation of this technology from research to safe, reliable and efficient clinical landing.
This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods.
P. Peykani, V. Charles, Ali Emrouznejad et al.· Archives of Computational Me...· 0 citations
This review provides a comprehensive overview of transformer-based model applications in genomics, transcriptomics, proteomics, drug discovery, drug discovery, and single-cell analysis, and highlights major challenges that remain insufficiently addressed in prior reviews.
Jiajia Liu, Mengyuan Yang, Yankai Yu et al.· Briefings in Bioinformatics· 41 citations· ⚡4