426 citations · 433 across the 11 of their papers we have counts for
11 papers
Exploration of Augmentation Strategies in Multi-modal Retrieval-Augmented Generation for the Biomedical Domain: A Case Study Evaluating Question Answering in Glycobiology
Primož Kocbek, Azra Frkatović-Hodžić, Dora Lalić +3
Multi-modal retrieval-augmented generation (MM-RAG) promises grounded biomedical QA, but it is unclear when to (i) convert figures/tables into text versus (ii) use optical characte…
UM_FHS at the CLEF 2025 SimpleText Track: Comparing No-Context and Fine-Tune Approaches for GPT-4.1 Models in Sentence and Document-Level Text Simplification
Primoz Kocbek, Gregor Stiglic
This work describes our submission to the CLEF 2025 SimpleText track Task 1, addressing both sentenceand document-level simplification of scientific texts. The methodology centered…
Plain language adaptations of biomedical text using LLMs: Comparision of evaluation metrics
Primoz Kocbek, Leon Kopitar, Gregor Stiglic
This study investigated the application of Large Language Models (LLMs) for simplifying biomedical texts to enhance health literacy. Using a public dataset, which included plain la…
Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions
Shaowei Guan, Hin Chi Kwok, Ngai Fong Law +3
Retrieval-augmented generation (RAG) has rapidly emerged as a transformative approach for integrating large language models into clinical and biomedical workflows. However, privacy…
UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent approach for plain language adaptations of biomedical text
Primoz Kocbek, Leon Kopitar, Zhihong Zhang +3
This paper describes our submissions to the TREC 2024 PLABA track with the aim to simplify biomedical abstracts for a K8-level audience (13-14 years old students). We tested three…
Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare
Leon Kopitar, Primoz Kocbek, Lucija Gosak +1
This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history o…
Two scholarly publishing cultures? Open access drives a divergence in European academic publishing practices
Leon Kopitar, Nejc Plohl, Mojca Tancer Verboten +3
The current system of scholarly publishing is often criticized for being slow, expensive, and not transparent. The rise of open access publishing as part of open science tenets, pr…
Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models
Leon Kopitar, Leon Bedrac, Larissa J Strath +2
This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models…
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI
Gregor Stiglic, Leon Kopitar, Lucija Gosak +5
Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve…
Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test
Simon Kocbek, Primoz Kocbek, Leona Cilar +1
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree…
Interpretability of machine learning based prediction models in healthcare
Gregor Stiglic, Primoz Kocbek, Nino Fijacko +3
There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions fo…