Researchers, clinicians, and engineers working at the crossroads of artificial intelligence and medicine got together once again this summer, and CCADD (Prof. Howard, David, Seeun) was there to join them. The occasion was the Conference on Health, Inference, and Learning (CHIL) 2026, a three-day gathering hosted in Seattle, Washington from June 28th through 30th.

Among a handful of research groups that were extended an invitation to the conference, Prof. Howard traveled together with his team Dr. David and Seeun. What made the trip especially meaningful this year was the publication of a paper from the lab, "Generation of Biingual Synthetic Clinical Notes for Realistic Data Augmentation," which the team unveiled to the community at the conference. Standing beside their poster, they walked visitors through the motivation and findings of the work, answering questions from curious attendees.

This year marked the seventh gathering of CHIL, jointly hosted by Sage Bionetworks and Seattle Children's Research Institute and hosted at the Institute's B. Wayne Hughes Building. As always, the program spanned a broad cross-section of issues shaping AI's role in healthcare. The conference opened with a keynote from Dr. Chris Longhurst, CEO of Seattle Children's, who spoke on "Evidence-based Medicine in the AI Era" and made the case for grounding AI adoption in real clinical implementation rather than hype. Dr. Pang Wei Koh followed with an invited talk on reinforcement learning approaches for deep research, and Dr. Jessilyn Dunn later delivered a keynote on wearable technology for disease detection titled "The Digital Physiome." A track devoted entirely to mental health also drew particular attention, weaving together talks on evaluating harm in AI safety, personalizing psychotherapy through language models, and building governance frameworks informed by practitioners, which were all capped off with a lively panel discussion.

For the team, the days in Seattle offered far more than a chance to share their research. They came away with new perspectives, new connections, and a renewed sense of how much further this field still has to grow.

CCADD members attended the 2026 Graduate School of Convergence Science and Technology Convergence Academic Festival, held on March 26, 2026, at the Faculty Hall of Seoul National University. This academic festival aimed to promote the exchange of research achievements among the Department of Applied Bioengineering, the Department of Molecular Medicine and Biopharmaceutical Sciences, and the Department of Health Science and Technology, while also providing a forum for presentation and discussion on bio-convergence research. The event brought together faculty members, researchers, and students from diverse fields to share recent research progress. The festival began with opening remarks delivered by Prof. Howard Lee, setting the stage for academic exchange among the participating departments.

More importantly, CCADD members presented one oral presentation and four poster presentations at the festival.

David gave an oral presentation titled “Mechanistic Reasoning Framework for Predicting Multi-Mechanism Pharmacokinetic Drug Interactions”. His work introduced a framework for predicting complex pharmacokinetic drug interactions involving multiple mechanisms.

Yujin’s poster “Annotation-Level Validation and Error Taxonomy for LLM-Assisted Korean–English Pharmaceutical Regulatory Corpus Construction”, focused on validation and error patterns in building a Korean–English pharmaceutical regulatory corpus using LLMs.

In a poster titled “LLM-Based Extraction of Adverse Drug Reactions from Real-World Clinical Notes”, Seoyoon presented an LLM-based approach for extracting adverse drug reaction information from real-world clinical notes.

Suhyun’s poster “Improving Causality Assessment of ADRs in Large Language Models through Rationale-Based Knowledge Distillation”, explored a method for improving the accuracy of causality assessment in LLMs through rationale-based knowledge distillation.

Seeun delivered a poster presentation titled “Generation of Multi-Lingual Synthetic Clinical Notes for Realistic Data Augmentation”, which proposed a framework for generating multilingual synthetic clinical notes to support realistic data augmentation.

Through this academic festival, CCADD members shared their recent research and exchanged ideas with researchers from related fields. The event provided an opportunity to present ongoing work, receive feedback, and engage in discussion on current topics in bio-convergence research.

A research article entitled “Beyond Fine-Tuning: Leveraging Domain-Aware In-Context Learning with Large Language Models for Clinical Named Entity Recognition” has been published in the Journal of Biomedical Informatics (Volume 174, February 2026). The Journal of Biomedical Informatics is a leading international journal in the field of biomedical and clinical informatics that publish high-impact research at the intersection of medicine, data science, and artificial intelligence. Our study presents a comprehensive evaluation of large language model (LLM) based in-context learning (ICL) for clinical named entity recognition (NER) under realistic clinical constraints.

The study compared ICL with conventional encoder-based fine-tuning approaches using manually annotated real-world clinical notes. ICL configurations were optimized across task instructions, output formats, demonstration selection methods, sorting strategies, and demonstration pool sizes using the open-source LLaMA-3.3-70B model, while encoder-based fine-tuning was performed with RoBERTa-large as the strongest baseline.

The results showed that optimized demonstration selection played a major role in determining ICL performance, and improved macro F1 scores compared to random selection. In moderate-resource settings, ICL outperformed encoder-based fine-tuning and remained competitive across a wide range of labeled data sizes. Moreover, ICL demonstrated superior data efficiency and achieved substantial gains in cross-institutional transfer without requiring any parameter updates.

Overall, the study highlights the potential of domain-aware ICL as a flexible and data-efficient alternative to encoder-based fine-tuning for clinical NER. By enabling rapid adaptation through demonstration pools rather than retraining, the proposed approach offers a practical solution for real-world clinical natural language processing in dynamic and resource-limited healthcare environments.

This research was supported by grants from the MSIT (IITP), the Ministry of Health and Welfare (KHIDI), and the Ministry of Education (NRF) of the Republic of Korea.

David Seung U Lee, a Ph.D. student at CCADD, and Seeun Park, an undergraduate researcher and the soon-to-be Master’s student at CCADD, received the Outstanding Presentation Awards at the 2025 Fall Conference of the Korean Society of Medical Informatics (KOSMI).

David presented his study titled "Improving Large Language Model-Based Drug-Drug Interaction Prediction Using Conformal Prediction." His research demonstrated that incorporating input features with coverage guarantees, verified through conformal prediction, significantly improves the performance of LLM-based DDI prediction in both in-context learning and fine-tuning settings.

Seeun Park, presented her study titled “Improving Synthetic Clinical Note Generation via Expert Prompting Reflecting Linguistic and Structural Characteristics.” Her research proposes a data augmentation framework that incorporates the knowledge of medical professionals into LLM-based prompting, coupled with a feedback-driven iterative generation loop. This approach enables the model to produce synthetic clinical notes that more closely reflect the characteristics of real-world clinical notes.

These awards highlight the growing importance of LLMs in the healthcare industry and recognizes the potential impact of our work in advancing AI-driven medical text generation.

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.

Suhyun An is a Master’s student at the Department of Molecular Medicine and Biopharmaceutical Science (MMBS), Graduate School of Convergence Science, Seoul National University.

Her research centers around Medical Decision Making and AI-based drug discovery using large language models and deep neural networks, with a focus on adverse drug reactions and drug–drug interactions.

Suhyun An participated in CCADD during the 2024 Winter Internship Program and gained experience in real world data preprocessing, and machine learning. After the internship, Suhyun An continued as an undergraduate researcher at CCADD and participated in projects analyzing real-world data, including electronic medical records and clinical notes. These projects improved skills in natural language processing, applications of Large Language Models, and programming languages like R, Python, and Structured Query Language (SQL).

Seeun Park is a Master’s student at the Department of Molecular Medicine and Biopharmaceutical Science (MMBS), Graduate School of Convergence Science, Seoul National University. She previously worked as an undergraduate researcher at CCADD after completing the 2024 Winter Internship Program. She majored in Bioengineering at Yonsei University. She was able to gain knowledge and hands-on experience on artificial intelligence for clinical drug development by the project “Improving the Generalizability of Natural Language Process Models to Extract Drug Safety Information from Clinical Notes”. With passion in healthcare and safety, she focuses on exploring clinical pharmacology and regulatory science more specifically Real-World Data (RWD) and policy analysis in Korea. It is her goal to work on expertise for the improvement of disease prevention and control.