Session 5: AI in research: enhancing writing, avoiding misconduct, and ensuring integrity

COURSE INFORMATION

This series is designed for all those interested in radiology research, with a particular focus on young radiologists eager to engage in scientific discovery. Whether exploring statistical best practices, navigating a research career, working with synthetic data, or ensuring research integrity, these sessions provide valuable insights and practical skills. Through expert-led discussions and real-world applications, participants will gain the knowledge needed to contribute meaningfully to the future of radiology research.

Large Language Models (LLMs) are transforming the way researchers conduct literature reviews, filter references, and write scientific manuscripts. While these tools offer efficiency and support, they also raise concerns about plagiarism, authorship integrity, and data reliability. This session will explore how to responsibly integrate LLM-based tools into research while maintaining ethical standards. Additionally, common forms of research misconduct will be discussed, including AI-assisted plagiarism, data manipulation, and authorship disputes, alongside institutional responsibilities and preventive measures.

LEARNING OBJECTIVES

•To review LLM-based tools for literature review, reference filtering, and scientific writing.
•To explore best practices for using AI tools responsibly in research.
•To understand different forms of research misconduct, including AI-assisted plagiarism and data fabrication.
•To examine real-life case studies of ethical violations and discuss possible sanctions.

PROGRAMME ONLINE

Tuesday, February 10, 2026