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Gastroenterology (GI) cancer surveillance scheduling relies on extracting structured data from unstructured clinical texts, such as endoscopy and pathology reports. Traditional Natural Language Processing (NLP) models have been employed for this task, but recent advancements in Large Language Models (LLMs) present a new opportunity for automation without requiring extensive labeled datasets. In this study, we propose an LLM-based entity extraction and rule-based decision support framework for Barrett&#39;s Oesophagus (BO) surveillance timing prediction. Our approach processes endoscopy and pathology reports to extract clinically relevant information and structures it into a standardised format, which is then used to determine appropriate surveillance intervals. We evaluate multiple state-of-the-art LLMs on real-world clinical datasets from two hospitals, assessing their performance in accuracy and running time cost. The results demonstrate that LLMs, particularly Phi-4 and (DeepSeek distilled) Qwen-2.5, can effectively automate the extraction of BO surveillance-related information with high accuracy, while Phi-4 is also efficient during inference. We also compared the trade-offs between LLMs and fine-tuned non-LLMs. Our findings indicate that LLM extraction based methods can support clinical decision-making by providing justifications from report extractions, reducing manual workload, and improving guideline adherence in BO surveillance scheduling.

ACL 2025

Improving Barrett&#39;s Oesophagus Surveillance Scheduling with Large Language Models: A Structured Extraction Approach

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Charles Lopresto, DO

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AMA Research Challenge 2022

Welcome to the largest national, multi-specialty research event featuring research from 750 medical students, residents and fellows, and international medical graduates.


Gastroenterology (GI) cancer surveillance scheduling relies on extracting structured data from unstructured clinical texts, such as endoscopy and pathology reports. Traditional Natural Language Processing (NLP) models have been employed for this task, but recent advancements in Large Language Models (LLMs) present a new opportunity for automation without requiring extensive labeled datasets. In this study, we propose an LLM-based entity extraction and rule-based decision support framework for Barrett's Oesophagus (BO) surveillance timing prediction. Our approach processes endoscopy and pathology reports to extract clinically relevant information and structures it into a standardised format, which is then used to determine appropriate surveillance intervals. We evaluate multiple state-of-the-art LLMs on real-world clinical datasets from two hospitals, assessing their performance in accuracy and running time cost. The results demonstrate that LLMs, particularly Phi-4 and (DeepSeek distilled) Qwen-2.5, can effectively automate the extraction of BO surveillance-related information with high accuracy, while Phi-4 is also efficient during inference. We also compared the trade-offs between LLMs and fine-tuned non-LLMs. Our findings indicate that LLM extraction based methods can support clinical decision-making by providing justifications from report extractions, reducing manual workload, and improving guideline adherence in BO surveillance scheduling.

Improving Barrett's Oesophagus Surveillance Scheduling with Large Language Models: A Structured Extraction Approach

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Large Language Models (LLMs) have significantly impacted medical Natural Language Processing (NLP), enabling automated information extraction from unstructured clinical texts. However, selecting the most suitable approach requires careful evaluation of different model architectures, such as generative LLMs and BERT-based models, along with appropriate adaptation strategies, including prompting techniques, or fine-tuning.

Prompting Large Language Models for Italian Clinical Reports: A Benchmark Study

Several studies explored different LLM implementations, highlighting their effectiveness in medical domain, including complex diagnostics patterns as for example in rheumatology. However, their application to Italian remains limited, serving as a key example of the broader gap in non-English language research. In this study, we present a task-specific benchmark analysis comparing generative LLMs and BERT-based models, on real-world Italian clinical reports. We evaluated zero-shot prompting, in-context learning (ICL), and fine-tuning across eight diagnostic categories in the rheumatology area. Results show that ICL improves performance over zero-shot-prompting, particularly for Mixtral and Gemma models. Overall, BERT fine-tuning present the highest performance, while ICL outperforms BERT in specific diagnoses, such as renal and systemic, suggesting that prompting can be a potential alternative when labeled data is scarce."

QoLAS: A Reddit Corpus of Health-Related Quality of Life Aspects of Mental Disorders

Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model's predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization.

Effective Multi-Task Learning for Biomedical Named Entity Recognition

This study explores the application of generative Large Language Models (LLMs) in DNA sequence analysis, highlighting their advantages over encoder-based models like DNABERT2 and Nucleotide Transformer. While encoder models excel in classification, they struggle to integrate external textual information. In contrast, generative LLMs can incorporate domain knowledge, such as BLASTn annotations, to improve classification accuracy even without fine-tuning. We evaluate this capability on antimicrobial resistance (AMR) gene classification, comparing generative LLMs with encoder-based baselines. Results show that LLMs significantly enhance classification when supplemented with textual information. Additionally, we demonstrate their potential in DNA sequence generation, further expanding their applicability. Our findings suggest that LLMs offer a novel paradigm for integrating biological sequences with external knowledge, bridging gaps in traditional classification methods.

Can Large Language Models Classify and Generate Antimicrobial Resistance Genes?

Case reports provide critical insights into rare and atypical diseases, but extracting structured knowledge remains challenging due to unstructured text and domain-specific terminology. We introduce CaseReportCollective, an LLM-extracted dataset of 85,961 open-access case reports spanning 37 years across 14 medical domains, validated through programmatic and human evaluation. Our dataset reveals key publication and demographic trends, including a significant increase in open-access case reports over the past decade, shifts in focus from oncology to COVID-19, and sex disparities in reporting across different medical conditions. Over time, the gap between male and female case reports has narrowed, suggesting greater equity in case reporting. Using \textbf{CaseReportCollective}, we further explore embedding-based retrieval for similar medical topics through accumulated similarity scores across extracted structured information. We also conducted detailed error analyses on the retrieval ranking, finding that high-reported topics dominate retrieval. Such retrieval is driven by lexical overlap rather than underlying clinical relevance, often failing to distinguish between semantically similar yet mechanistically distinct conditions. Future work should focus on clinical-aware embeddings adjusted for long-tailed case distributions to improve retrieval accuracy.

CaseReportCollective: A Large-Scale LLM-Extracted Dataset for Structured Medical Case Reports

Antibiotic resistance identification is essential for public health, medical treatment, and drug development. Traditional sequence-based models struggle with accurate resistance prediction due to the lack of biological context. To address this, we propose an NLP-based model that integrates genetic sequences with structured textual annotations, including gene family classifications and resistance mechanisms. Our approach leverages pretrained language models for both genetic sequences and biomedical text, aligning biological metadata with sequence-based embeddings. We construct a novel dataset based on the Antibiotic Resistance Ontology (ARO), consolidating gene sequences with resistance-related textual information. Experiments show that incorporating domain knowledge significantly improves classification accuracy over sequence-only models, reducing reliance on exhaustive laboratory testing. By integrating genetic sequence processing with biomedical text understanding, our approach provides a scalable and interpretable solution for antibiotic resistance prediction.

Enhancing Antimicrobial Drug Resistance Classification by Integrating Sequence-Based and Text-Based Representations

High-quality scientific article embeddings are essential for tasks like document retrieval, citation recommendation, and classification. Traditional citation-based approaches assume citations reflect semantic similarity—an assumption that introduces bias and noise. Recent models like SciNCL and SPECTER2 have attempted to refine citation-based representations but still struggle with noisy citation edges and fail to fully leverage textual information. To address these limitations, we propose a hybrid approach that combines Finding-Citation Graphs (FCG) with contrastive learning. Our method improves triplet selection by filtering out less important citations and incorporating finding similarity relations, leading to better semantic relationship capture. Evaluated on the SciRepEval benchmark, our approach consistently outperforms citation-only baselines, showing the value of text-based semantic structures. While we do not surpass state-of-the-art models in most tasks, our results reveal the limitations of purely citation-based embeddings and suggest paths for improvement through enhanced semantic integration and domain-specific adaptations.

Beyond Citations: Integrating Finding-Based Relations for Improved Biomedical Article Representations

Case Report Forms (CRFs) are largely used in medical research as they ensure accuracy, reliability, and validity of results in clinical studies. However, publicly available, well-annotated CRF datasets are scarce, limiting the development of CRF slot filling systems able to fill in a CRF from clinical notes. To mitigate the scarcity of CRF datasets, we propose to take advantage of available datasets annotated for information extraction tasks and to convert them into structured CRFs. We present a semi-automatic conversion methodology, which has been applied to the E3C dataset in two languages (English and Italian), resulting in a new, high-quality dataset for CRF slot filling. Through several experiments on the created dataset, we report that slot filling achieves 59.7% for Italian and 67.3% for English on a closed Large Language Models (zero-shot) and worse performances on three families of open-source models, showing that filling CRFs is challenging even for recent state-of-the-art LLMs.

Converting Annotated Clinical Cases into Structured Case Report Forms

Accurate prediction of drug–target interactions is critical for accelerating drug discovery. In this work, we frame drug–target prediction as a link prediction task on heterogeneous biomedical knowledge graphs (KG) that integrate drugs, proteins, diseases, pathways, and other relevant entities. Conventional KG embedding methods such as TransE and ComplEx-SE are hindered by their reliance on computationally intensive negative sampling and their limited generalization to unseen drug–target pairs. To address these challenges, we propose Multi-Context-Aware Sampling (MuCoS), a novel framework that prioritizes high-density neighbours to capture salient structural patterns and integrates these with contextual embeddings derived from BERT. By unifying structural and textual modalities and selectively sampling highly informative patterns, MuCoS circumvents the need for negative sampling, significantly reducing computational overhead while enhancing predictive accuracy for novel drug–target associations and drug targets. Extensive experiments on the KEGG50k and PharmKG-8k datasets demonstrate that MuCoS outperforms baselines, achieving up to a 13% improvement in MRR for general relation prediction on KEGG50k, a 22% improvement on PharmKG-8k, and a 6% gain in dedicated drug–target relation prediction on KEGG50k.

MuCoS: Efficient Drug–Target Discovery via Multi-Context-Aware Sampling in Knowledge Graphs

Named Entity Recognition (NER) is crucial for extracting domain-specific entities from text, particularly in biomedical and chemical fields.

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