Primož Kocbek

Gregor Stiglic, Leon Kopitar, Lucija Gosak et al.

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 the above-mentioned problem, we employed generative artificial intelligence techniques based on large-scale language models to summarize abstracts of scientific papers. Our objective is to investigate the potential of generative artificial intelligence in diminishing the cognitive load experienced by practitioners, thus exploring its ability to alleviate mental effort and burden. The study participants were provided with two use cases related to preventive care and behavior change, simulating a search for new scientific literature. The study included 113 university students from Slovenia and the United States randomized into three distinct study groups. The first group was assigned to the full abstracts. The second group was assigned to the short abstracts generated by AI. The third group had the option to select a full abstract in addition to the AI-generated short summary. Each use case study included ten retrieved abstracts. Our research demonstrates that the use of generative AI for literature review is efficient and effective. The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts. The results, however, also show significantly lower accuracy in extracted knowledge in cases where full abstract was not available. Such a disruptive technology could significantly reduce the time required for healthcare professionals to keep up with the most recent scientific literature; nevertheless, further developments are needed to help them comprehend the knowledge accurately.

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 language adaptations of biomedical abstracts, we developed and evaluated several approaches, specifically a baseline approach using a prompt template, a two AI agent approach, and a fine-tuning approach. We selected OpenAI gpt-4o and gpt-4o mini models as baselines for further research. We evaluated our approaches with quantitative metrics, such as Flesch-Kincaid grade level, SMOG Index, SARI, and BERTScore, G-Eval, as well as with qualitative metric, more precisely 5-point Likert scales for simplicity, accuracy, completeness, brevity. Results showed a superior performance of gpt-4o-mini and an underperformance of FT approaches. G-Eval, a LLM based quantitative metric, showed promising results, ranking the approaches similarly as the qualitative metric.

Leon Kopitar, Primoz Kocbek, Lucija Gosak et al.

This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history of text summarization from the 1950s to the introduction of pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) and Generative Pre-training Transformers (GPT) are presented. In total, 60 studies were identified in PubMed and Web of Science, of which 29 were excluded and 24 were read and evaluated for eligibility, resulting in the use of seven studies for further analysis. This chapter also includes a section with examples including an example of a comparison between GPT-3 and state-of-the-art GPT-4 solutions in scientific text summarisation. Natural language processing has not yet reached its full potential in the generation of brief textual summaries. As there are acknowledged concerns that must be addressed, we can expect gradual introduction of such models in practise.

Primoz Kocbek, Leon Kopitar, Zhihong Zhang et al.

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 approaches using OpenAI's gpt-4o and gpt-4o-mini models: baseline prompt engineering, a two-AI agent approach, and fine-tuning. Adaptations were evaluated using qualitative metrics (5-point Likert scales for simplicity, accuracy, completeness, and brevity) and quantitative readability scores (Flesch-Kincaid grade level, SMOG Index). Results indicated that the two-agent approach and baseline prompt engineering with gpt-4o-mini models show superior qualitative performance, while fine-tuned models excelled in accuracy and completeness but were less simple. The evaluation results demonstrated that prompt engineering with gpt-4o-mini outperforms iterative improvement strategies via two-agent approach as well as fine-tuning with gpt-4o. We intend to expand our investigation of the results and explore advanced evaluations.

Simon Kocbek, Primoz Kocbek, Leona Cilar et al.

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 to which a human can understand the cause of decisions reached by a ML model. Interpretability is of extremely high importance in many fields of healthcare due to high levels of risk related to decisions based on ML models. Calibration of the ML model outputs is another issue often overlooked in the application of ML models in practice. This paper represents an early work in examination of prediction model calibration impact on the interpretability of the results. We present a use case of a patient in diabetes screening prediction scenario and visualize results using three different techniques to demonstrate the differences between calibrated and uncalibrated regularized regression model.