OBJECTIVES: Patients with rare diseases often face long delays before receiving a diagnosis. Using electronic health records for automated phenotyping and diagnosis of rare diseases is a promising approach but can be challenging because critical information is often recorded in unstructured notes rather than structured fields. This systematic review synthesizes the current literature applying natural language processing (NLP) and large language models (LLMs) for rare disease phenotyping and diagnosis from clinical text. MATERIALS AND METHODS: A systematic search was conducted in PubMed, ACM Digital Library, and IEEE Xplore. Two reviewers independently screened papers and extracted data. Methodological rigor and quality of the studies were evaluated using the MI-CLAIM framework. RESULTS: The search resulted in 135 studies; 27 of them met the inclusion criteria. Methods used spanned rule-based systems, classical ML/DL models, transformer architectures, and LLMs. Transformer- and LLM-based approaches outperformed earlier methods in entity recognition, phenotype extraction, and diagnostic ranking. Several studies demonstrated clinical impact, such as increased genetic testing and identification of undiagnosed cases. However, most studies relied on retrospective and single-center datasets. Reporting of preprocessing, evaluation, and reproducibility was largely inconsistent, and interpretability, fairness, and privacy were rarely addressed. DISCUSSION: Natural language processing and LLMs show strong potential to accelerate rare disease diagnosis. However, heterogeneity in methods and metrics hinders cross-study comparability. Data scarcity, lack of generalization, and limited transparency remain significant challenges. CONCLUSIONS: Natural language processing/LLM methods can support timely diagnosis of rare diseases using unstructured clinical text. Future research should prioritize multicenter studies, standardized evaluation frameworks, transparency, and fairness safeguards to enable reliable, equitable deployment.

As the ageing population grows and information technology evolves, elderly individuals increasingly rely on interacting with intelligent interfaces in their daily activities, posing a challenge to their interaction fluency. This study aims to identify the evaluation indicators and conditions in intelligent interactive interfaces that affect the operational performance and subjective preferences of elderly users. From there, general conclusions and strategies are summarised to promote the development of age-friendly design and practice in intelligent interactive interfaces. This systematic review includes 31 studies for analysis and classifies them into four categories according to the form of interaction: gesture interaction, voice interaction, multimodal interaction, and artificial intelligence interaction. The review indicates that factors such as the simplicity and direction of gestures in gesture interaction, the fluency of user expression and system recognition in voice interaction, modalities of interface input and feedback, as well as the conversational style, appearance, and mode of artificial intelligence, all affect user performance and preferences. However, there may be inconsistencies in the performance and preferences of elderly users in almost every form of interface. Therefore, future study needs to delve into specific scenarios for analysis.

Objectives: The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. This review aims to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type. Results: A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility. Discussion: The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while providing a better understanding of its complex structures. It can potentially drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is needed to discuss and overcome limitations, enhancing model transparency and applicability.

OBJECTIVES: Extracting PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method, PICOX, to extract overlapping PICO entities. MATERIALS AND METHODS: PICOX first identifies entities by assessing whether a word marks the beginning or conclusion of an entity. Then, it uses a multi-label classifier to assign one or more PICO labels to a span candidate. PICOX was evaluated using 1 of the best-performing baselines, EBM-NLP, and 3 more datasets, ie, PICO-Corpus and randomized controlled trial publications on Alzheimer's Disease (AD) or COVID-19, using entity-level precision, recall, and F1 scores. RESULTS: PICOX achieved superior precision, recall, and F1 scores across the board, with the micro F1 score improving from 45.05 to 50.87 (P ≪.01). On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56.66 to 67.33. On the COVID-19 dataset, PICOX also outperformed the baseline and improved the micro F1 score from 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores with higher precision when compared to the baseline. CONCLUSION: PICOX excels in identifying overlapping entities and consistently surpasses a leading baseline across multiple datasets. Ablation studies reveal that its data augmentation strategy effectively minimizes false positives and improves precision.