How to Write Effective Prompts for Screening Biomedical Literature Using Large Language Models

BioMedInformatics, Journal Year: 2025, Volume and Issue: 5(1), P. 15 - 15

Published: March 11, 2025

Large language models (LLMs) have emerged as powerful tools for (semi-)automating the initial screening of abstracts in systematic reviews, offering potential to significantly reduce manual burden on research teams. This paper provides a broad overview prompt engineering principles and highlights how traditional PICO (Population, Intervention, Comparison, Outcome) criteria can be converted into actionable instructions LLMs. We analyze trade-offs between “soft” prompts, which maximize recall by accepting articles unless they explicitly fail an inclusion requirement, “strict” demand explicit evidence every criterion. Using periodontics case study, we illustrate design affects recall, precision, overall efficiency discuss metrics (accuracy, F1 score) evaluate performance. also examine common pitfalls, such overly lengthy prompts or ambiguous instructions, underscore continuing need expert oversight mitigate hallucinations biases inherent LLM outputs. Finally, explore emerging trends, including multi-stage pipelines fine-tuning, while noting ethical considerations related data privacy transparency. By applying rigorous evaluation, researchers optimize LLM-based processes, allowing faster more comprehensive synthesis across biomedical disciplines.

Language: Английский

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Sustainable Innovation in Healthcare

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 277 - 298

Published: April 24, 2025

Large Language Models like transformers and chat GPT have brought about a considerable shift in the healthcare system areas support for clinical decision, education of patients, diagnosis. These models been used different applications such as Natural Processing (NLP), medical image analysis, Electronic Health Record (EHR). However, involvement LLMs has some challenges because importance information that makes any error critical, hence rigorous evaluation is required to prevent error. This Chapter provides an extensive literature review LLMs' use demonstrate how these may contribute profound changes improvements processes studies. Besides highlighting many beneficial uses LLMs, this paper further presents ethical issues sustainability data privacy, bias necessity adequate validation.

Language: Английский

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Large Language Models in Systematic Review Screening: Opportunities, Challenges, and Methodological Considerations

Information, Journal Year: 2025, Volume and Issue: 16(5), P. 378 - 378

Published: May 1, 2025

Systematic reviews require labor-intensive screening processes—an approach prone to bottlenecks, delays, and scalability constraints in large-scale reviews. Large Language Models (LLMs) have recently emerged as a powerful alternative, capable of operating zero-shot or few-shot modes classify abstracts according predefined criteria without requiring continuous human intervention like semi-automated platforms. This review focuses on the central challenges that users biomedical field encounter when integrating LLMs—such GPT-4—into evidence-based research. It examines critical requirements for software data preprocessing, discusses various prompt strategies, underscores continued need oversight maintain rigorous quality control. By drawing current practices cost management, reproducibility, refinement, this article highlights how teams can substantially reduce workloads compromising comprehensiveness inquiry. The findings presented aim balance strengths LLM-driven automation with structured checks, ensuring systematic retain their methodological integrity while leveraging efficiency gains made possible by recent advances artificial intelligence.

Language: Английский

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Harnessing Large Language Models for Personalized Learning: A Case Study in Algorithm Education at Mu’tah University

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 117 - 128

Published: Jan. 1, 2025

Language: Английский

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Secformer: privacy-preserving atomic-level componentized transformer-like model with MPC

Digital Communications and Networks, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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