Search

Transformer-based architectures have significantly advanced clinical natural language processing by improving the capture of contextual relationships in unstructured electronic health records compared to earlier recurrent and convolutional models, with domain-specific variants such as ClinicalBERT and BioBERT designed to better handle clinical terminology, abbreviations, and specialized language, thereby improving information extraction performance, although the relative impact of different pre-training strategies remains insufficiently synthesized and requires systematic evaluation of corpus selection and fine-tuning approaches; this systematic review mapped studies focusing on pre-training corpora, fine-tuning methods, and named entity recognition performance across entity types such as medications, diseases, procedures, laboratory tests, and social determinants of health, using PRISMA-guided methods and searches across PubMed, ACL Anthology, arXiv, and IEEE Xplore, identifying 32 eligible studies from 1,247 records; findings showed that ClinicalBERT, BioBERT, and PubMedBERT were the most frequently evaluated models, pre-trained on datasets such as MIMIC-III, PubMed abstracts, and mixed biomedical corpora, with consistent evidence that domain-specific pre-training outperforms general-domain BERT models on benchmarks like i2b2 and n2c2 despite variation across entity types and fine-tuning strategies, while clinical pre-training on large EHR corpora improves named entity recognition and optimized fine-tuning approaches such as lower learning rates and data augmentation further enhance performance, particularly for medications and diseases, underscoring the importance of domain adaptation and the need for more standardized evaluation protocols in clinical NLP research.

Patient no-shows in outpatient clinics (5%–30% across specialties) disrupt scheduling efficiency, increase wait times, and strain healthcare resources. To address this, healthcare systems are increasingly applying machine learning (ML) for predictive scheduling support. This systematic review synthesizes ML approaches for predicting outpatient no-shows, focusing on model types, feature usage, and reported operational deployment outcomes, with emphasis on translation into clinical scheduling practice. A PRISMA-compliant search of PubMed, Embase, IEEE Xplore, Scopus, and Web of Science identified studies using ML for no-show prediction in outpatient settings. Data on models, features, performance, and implementation were extracted. Risk of bias was assessed using an adapted PROBAST tool. Thirty-two studies were included. Logistic regression, random forest, and XGBoost were the most commonly used models. Historical attendance data was the dominant predictive feature. Fewer than 20% of studies reported real-world implementation, and reported intervention outcomes (e.g., overbooking, reminders) were inconsistent. While ML models show strong predictive performance, real-world deployment and evidence of operational impact remain limited. This gap highlights the need to prioritize implementation-focused research to translate predictive accuracy into measurable improvements in clinic efficiency and access.

Alzheimer’s disease (AD) is the leading cause of dementia, affecting over 50 million people worldwide, with prevalence expected to triple by 2050. Early detection is crucial for clinical trial enrollment and care planning, and multimodal data (MRI, PET, CSF biomarkers, and cognitive assessments) provides complementary information on neurodegeneration, metabolism, and protein aggregation. This systematic review synthesizes AI/ML approaches for early AD detection using multimodal data, focusing on fusion strategies and performance across disease stages. Following PRISMA guidelines, searches of PubMed, IEEE Xplore, Scopus, Web of Science, and arXiv (2017–2023) identified studies using ML/DL with at least two modalities and reporting diagnostic performance. From 1,247 records, 35 studies were included. MRI was the most used modality (>90%), followed by cognitive tests (70–80%), PET (40–50%), and CSF (20–30%). Early fusion was most common, with increasing use of intermediate fusion. Multimodal models achieved AUROC of 0.90–0.98 for AD vs controls, but lower performance (0.70–0.85) for predicting MCI conversion to AD. Overall, multimodal AI improves early AD detection, with strong performance for diagnosis but persistent challenges in forecasting MCI progression due to heterogeneity and limited longitudinal data.