CCADD members attended the 2025 Fall Conference of the Korean Society of Medical Informatics(KOSMI), held from November 23 to 24 at Songdo Convensia in Incheon. With the theme “Generative AI in Healthcare Systems: From Insight to Impact,” the conference showcased the latest developments, real-world applications, and future opportunities for generative AI in healthcare. Major sessions explored LLM evaluation, LLM-based clinical ation and validation, and AI-driven prediction of neurological disorders.

The conference featured keynote lectures from leading experts in Korea and Taiwan. Professor Sang-Hoon Jeon from Seoul National University Bundang Hospital discussed initiatives aimed at reducing global healthcare disparities, including the establishment of a Global Smart Hospital Network and the development of cloud-based platforms for clinician education and severe disease consultation. He emphasized that the key challenge is not merely how AI can reduce healthcare disparities, but how quickly high-quality medical services can reach those who are waiting for them. Professor Ming-Chin Lin from the College of Medicine at Taipei Medical University addressed the shared challenges facing both Korea and Taiwan—rapid population aging, declining birth rates, and increasing strain on healthcare professionals. He introduced Ambient Intelligence as the next evolution beyond ambient recording and traditional AI systems: a context-aware, ethically governed, sensor-integrated healthcare environment designed to support clinicians and empower patients.

More importantly, CCADD delivered five oral presentations.

David presented his work on “improving the performance of drug interaction prediction using conformal prediction”, which delved into a new method of selecting input for LLM-based prediction module using the statistical framework. 

Yujin presented her work on “Constructing a Domain-Specific Korean–English Parallel Corpus and Dictionary for Medical Translation Using a Large Language Model,” which aims to improve the reliability and consistency of LLM-based medical translation. By introducing a large-scale corpus construction framework, she provided foundational resources that can support future research in the field. During the session, she exchanged insights with reviewers and fellow researchers and explored ways to further expand and deepen the study.

Suhyun An presented her research on “Investigating LLM-based Reasoning and Training Strategies for automating causality assessment of adverse drug reactions”. Her study systematically explored optimal reasoning strategies and training methods to enable large language models to emulate the clinical decision-making process of medical experts.

Seoyoon Jang presented her work on "LLM-Based Extraction of Adverse Events from Real-World Clinical Notes", which leveraged open-source LLMs to build a privacy-preserving and robust framework capable of extracting adverse events from real-world clinical notes.

Seeun Park presented her work titled “Improving Synthetic Clinical Note Generation via Expert Prompting Reflecting Linguistic and Structural Characteristics”. Her research proposed a new synthetic data-augmentation framework that captures the characteristics of real-world clinical notes, demonstrating its potential to support large-scale, high-quality synthetic datasets for AI training.

Through this conference, CCADD members reaffirmed that their research aligns with the current interest in the generative AI, which will play a pivotal role in advancing the healthcare industry. The event strengthened our commitment to pursuing impactful research and contributing to the development of next-generation AI-driven healthcare systems.