Aug 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 97 references
TL;DR
This review provides researchers and practitioners with a structured framework for method selection based on their specific constraints and identifies six prioritized research directions for future investigation, identifying critical research gaps including the preservation of multi-word clinical concepts, scarce evaluation in real-world clinical workflows, and persistent hallucination risks in model-based extraction.
A knowledge graph generation pipeline that extracts subject-predicate-object triplets representing scientific claims from research articles and implements a multi-stage consolidation process focused on normalizing entities and relations, offering a robust framework for reducing ambiguity and improving the interoperabil...
S. Krovvidi, Finn Vos, Laurent D. Hasson et al.· AHFE International· 0 citations
Current evidence supports supervised, task-specific applications of LLMs for extraction, triage, retrieval, and documentation rather than autonomous pharmacovigilance decision-making, highlighting their potential for precision medicine and big data-enabled safety monitoring.
Tae You Kim, Won-Sik Oh, Dong-Hwa Jeong· Diagnostics· 0 citations
This is the first study to benchmark SLMs on Italian EHRs and investigate the role of clinical expertise in prompt engineering, offering valuable insights for the future integration of SLMs into real-world clinical workflows.
Federica Corso, V. Peppoloni, L. Mazzeo et al.· Communications Medicine· 0 citations
Clinical text is an important part of healthcare systems because it is used to store and manage patient information in documents such as discharge summaries, doctor notes, and diagnostic reports. Among these documents, discharge summaries are especially important because they provide a brief overview of a patient’s dia...
Mohammad Imran, M. Irfan, Sajida Sultana.Sk et al.· 2026 7th International Confe...· 0 citations
This study evaluated both closed-source and open-source large language models for extracting and prioritizing medical jargon from EHR notes relevant to individual patients, leveraging prompting techniques, fine-tuning, and data augmentation and found that model performance could deviate largely based on prompting style...
W. Jang, Sharmin Sultana, Zonghai Yao et al.· JMIR AI· 1 citation
Overall, the evidence suggests that success in lower capability levels does not consistently translate to higher-level reasoning, especially when reports are inconsistent, required staging inputs are missing, or clinical assumptions must be inferred, which helps explain gaps between benchmark results and practical adop...
Maryam Seifaddini, Mohammad Beheshti, Steven Richberg et al.· Modern Pathology· 0 citations
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