A research article entitled "Design and Evaluation of an Automated Pediatric Acute Lymphoblastic Leukemia Registry from Clinical Data Warehouses" has been published in BMC Medical Informatics and Decision Making (2026). BMC Medical Informatics and Decision Making is a leading international journal that publishes high-impact research on the development, implementation, and evaluation of medical informatics, clinical decision-making systems, and health information technologies. Our study presents a comprehensive framework for constructing a high-quality, scalable pediatric acute lymphoblastic leukemia (ALL) registry through automated extraction of electronic medical record (EMR)-based real-world data (RWD) from clinical data warehouses (CDWs).

The study developed PeARL (Pediatric ALL Registry using eLectronic medical record-based RWD) by integrating standardized mapping, multivariate transformations, and rule-based natural language processing (NLP) to capture clinically relevant variables from two university-affiliated tertiary-care hospitals in South Korea: Seoul National University Hospital (SNUH) and the Catholic Medical Center (CMC). A total of 1,609 pediatric patients with ALL (663 from SNUH and 946 from CMC) were included. Data quality was systematically evaluated using 228 rules across five dimensions (completeness, validity, accuracy, uniqueness, consistency).

The results showed that the automated data extraction proportion reached 89.7% at SNUH and 75.0% at CMC, with most elements processed through single-field transformations. After applying the quality management process, the overall error rate decreased from 1.858% to 0.001% at SNUH and from 0.129% to 0.001% at CMC, corresponding to an estimated reliability of 99%. Moreover, the registry demonstrated robust multicenter applicability through standardized table specifications and cross-site review, despite structural differences between institutional CDWs.

Overall, the study highlights the potential of a clinically guided, standardized framework as a reproducible and scalable solution for registry construction. By combining structured mapping, multivariate transformation, and rule-based NLP, the proposed approach offers a practical pathway for multicenter research and regulatory-grade real-world evidence generation.

A new research article, entitled “Assessing the representativeness of single-center EMR data on ten cancer types: A comparative analysis with national statistics from South Korea (2011–2021)”, authored by Won Jung-Hyun and Howard Lee, was published in the International Journal of Medical Informatics(IJMI). IJMI is a leading international journal that publishes high-impact research on the development, implementation, and real-world evaluation of health information systems and clinical informatics technologies.

This study evaluated whether single-center electronic medical record (EMR) data from Seoul National University Hospital (SNUH) could represent a real-world data source for oncology research in South Korea. To address this, the study analyzed annual incidence and prevalence patterns for ten major cancer types (e.g., breast, gallbladder/biliary tract, gastric, kidney, liver, lung, pancreatic, prostate, thyroid cancers, and leukemia) from 2011 to 2021 using national cancer statistics from the Korean Statistical Information Service (KOSIS) as reference. The study also examined age and gender distributions between two datasets. The results showed that the SNUH EMR data included 8.2% of national incident cancer cases and 10.7% of prevalent cases. The study also found no significant differences in age and gender distributions across all cancer types between the SNUH EMR and national data. In addition, strong correlations in annual trends were observed for several major cancers, including breast, lung, and pancreatic cancers. Overall, this study showed that single-center EMR data can be a valuable, representative clinical resource for oncology research in South Korea. At the same time, the study emphasizes that the underrepresentation of certain cancer types and external temporal factors, such as changes in clinical guidelines or major healthcare disruptions, should be carefully considered when generalizing findings from a single institution to broader populations.

Won J-H, Lee H. Assessing the representativeness of single-center EMR data on ten cancer types: A comparative analysis with national statistics from South Korea (2011–2021). International Journal of Medical Informatics (2026): 106401

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.

Join us in congratulating David Seung U Lee on his successful graduation with a PhD in Medical Science!

On February 25, David attended the 80th Commencement Ceremony of Seoul National University (SNU), where he received his PhD degree under the supervision of Prof. Howard Lee. He was also awarded with the Best Dissertation Award in recognition of his outstanding research achievements. His dissertation was entitled “Mechanistic Understanding and Prediction of Drug–Drug Interactions in Humans using LLM-Based Multimodal Explainable Framework”.

Dr. Lee is the first PhD graduate of CCADD, who received his degree in the Department of Molecular Medicine and Biopharmaceutical Sciences at the Graduate School of Convergence Science and Technology, Seoul National University. He will continue his academic career at CCADD as a postdoctoral researcher, focusing on clinical natural language processing and artificial intelligence–based drug development.

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.

On Friday, December 26, 2025, CCADD had the annual Year-End Workshop at Seoul National University, Seoul, South Korea. The workshop brought together all current CCADD members, part-time students and winter interns. Throughout a full day of presentations and discussions, CCADD members reflected on the year 2025 and looked ahead to 2026.

The workshop began with arrival and reception, followed by a series of presentations covering research progress, academic development, and practical lessons learned. As the first speaker, Seoyoon opened the program with a session titled “Looking Back on the Year 2025,” highlighting CCADD’s accomplishments in 2025 and celebrating the achievements of its members.

The yearly report was followed by Suhyun, who shared key takeaways from a year of full-time work at CCADD across two phases, from undergraduate research to the first semester of the master’s program. Then, Seeun presented “Preparation for the 1st Oral Presentation,” reflecting on her year-long research and walking through her conference-presentation preparation process. After that, Sunyoung delivered a presentation titled “Life in the Industry vs. Graduate School,” in which she introduced the role of a Clinical Project Leader at Daewoong Pharmaceutical Company highlighting key differences between life in industry and life as a master’s student. As the last session before lunch, David delivered a talk about the things he wished he had known early on, including having a daily habit of reading papers and making a usable personal template to take notes of what you learned from papers.

Following the morning sessions, participants took a break for lunch and then toured the main library at Seoul National University’s Gwanak Campus, exploring its study spaces, archives, and facilities. After the tour, members gathered for a photo time and captured group photos inspired by this year’s Pantone Color theme.

Soyoung presented “How I Learned Actually to Write” and explained what she learned while writing during the second half of 2025 under the supervision of Prof. Howard Lee. Then, Sungwook delivered a workshop talk titled “How I Became the Pre-crastinator at CCADD.” In the talk, he defined a “pre-crastinator” as someone who finishes tasks early and stays in the lab late to keep studying. It was followed by Chaeyoung, who shared her journey for the 2025 Google Solution Challenge, from ideation and development to presenting at the Asian Development Bank in the Philippines. She also reflected on pitching on an international stage and the lessons learned through receiving the award.

After a short break, the workshop continued with sessions focused on practical milestones and next-year planning. In the afternoon session, Seoyoon presented “First Grant Proposal: What I Learned and What I Wish I’d Known,” sharing key challenges and lessons from her first grant project management experience. In the tenth session, Yujin led “Looking Ahead to 2026 at CCADD,” presenting a vision for the center’s research trajectory in the coming year.  It also compiled target conferences for future submissions and concluded with a shared aspiration for deeper research in 2026.

Moreover, in the session, "AI 2027: Where we were, where we are, and where we will be", David went over the prospective report, "AI 2027," in which the authors made predictions on what will happen to the state of AI arms race between US and China. In addition to peeking into the future of AI, David emphasized the need for shifting gears toward using agentic AIs and understanding their inner workings by briefly discussing important developments in the field of agentic AI.

The most significant part of the workshop came in the closing talk, where Prof. Howard presented a paradigm shift from +AI to AI+. He contrasted a domain-first approach with an AI first approach and explaining how research strategy changes under an AI+ framework. He emphasized CCADD’s future direction toward AI-centered approach and proposed holding a “CCADD AI+ Conversion Meeting” in the early 2026 to brainstorm and define core research themes and strategies.

The workshop concluded with closing remarks from Prof. Howard, and everyone gathered for dinner. The 2025 CCADD Year-End Workshop provided members with an opportunity to exchange ideas, reflect on progress, and align on shared goals for the year ahead.

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.