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.

Convergence Newsletter, published by the Graduate School of Convergence Science and Technology, Seoul National University, featured two congratulatory events from CCADD.

The first news article introduced Medicall, an AI-based emergency medical solution developed by Chaeyoung Chang, an undergraduate researcher at CCADD. Chaeyoung received the Most Societal Impact Award, one of the Top 3 honors at the 2025 Asia-Pacific Google Solution Challenge, co-hosted by the Asian Development Bank (ADB) and Google, which is a global competition that brings together student developers from Google Developer Groups on Campus to design AI based projects that contribute to the United Nations Sustainable Development Goals (UN SDGs). As a member of Team Atempo, Chaeyoung developed Medicall, an emergency medical service that helps paramedics quickly locate hospitals with available beds during patient transport.

Using Google’s Gemini, Medicall analyzes a patient’s condition and location in real time and recommends the most suitable emergency department within one minute. This enables patients to receive timely treatment within the critical golden hour, improving survival rates and enhancing the efficiency of emergency care systems. The project was praised for its creativity and practical impact, demonstrating how artificial intelligence can be applied to address real healthcare challenges. It was later featured in a news article introducing Medicall as an innovative AI solution to the emergency room crisis in Korea.

The second news article shared Chaeyoung’s experience during the 2025 Summer Convergence Research Program at CCADD, where she took part in two projects that used artificial intelligence to improve the efficiency of drug development. In the first project, she studied how GLP-1 receptor agonists (GLP-1RA) can help prevent nonalcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes by analyzing real-world clinical data. In the second, she worked on developing a machine translation model for regulatory s in the pharmaceutical and medical device fields.

Through these projects, Chaeyoung gained practical experience in data analysis and AI model development. Reflecting on her internship, she said that the structured lectures, mentorship, and book reading sessions on Python and large language models helped her build a strong foundation in AI and strengthened her motivation to pursue graduate research in this field. She expressed her gratitude to the Graduate School of Convergence Science and Technology and to all CCADD members for their guidance and support throughout the program.

Prof. Howard and his team (David and Yujin) presented their works at the 2025 American College of Clinical Pharmacology (ACCP) Annual Meeting, held in Phoenix, Arizona, from September 14–16, 2025. Under the theme “Leveraging Innovative Approaches to Transform Clinical Pharmacology,” the conference gathered experts to exchange insights on the evolving landscape in the field of clinical pharmacology.

The team delivered three poster presentations: 1) “Optimized In-Context Learning Outperforms Finetuning for Clinical Information Extraction across Diverse Healthcare Settings,” 2) “A Set of Inputs with Coverage Guarantee Enhances the Prediction of Drug Interactions,” and 3) “Constructing a Domain-Specific English–Korean Parallel Dictionary for Clinical Pharmacology and Regulatory Science Using a Large Language Model”. The presentations attracted considerable attention and prompted engaging exchanges among participants. The team’s research showed that in-context learning can serve as a data-efficient alternative to finetuning for clinical information extraction, offering practical guidance for applying large language models in healthcare settings with limited resources. They also demonstrated that incorporating input features with coverage guarantees, validated through conformal prediction, enhances the performance of LLM-based drug–drug interaction prediction. Lastly, the team showed that a domain-specific English–Korean parallel dictionary helps overcome language barriers and strengthens communication in clinical pharmacology and regulatory science.

As for the sessions in the ACCP conference, it began with a plenary session on optimizing patient outcomes through individualized pharmacotherapy. Subsequent symposia explored a wide range of critical topics, including diversity plans outlined by the U.S. Food and Drug Administration (FDA), therapeutic strategies for maternal and pediatric populations, innovations in dosing for preterm infants, therapeutic drug monitoring for biologics, and the application of artificial intelligence. Each meeting offered a clear picture of where clinical pharmacology is heading. The focus on individualized pharmacotherapy and model-informed drug development showed how artificial intelligence and mechanistic modeling are reshaping trial design and outcome prediction. At the same time, discussions on therapeutic drug monitoring for biologics and dosing in preterm infants revealed the field’s growing ability to tailor therapy for vulnerable populations.

Collectively, the conference underscored the expanding role of clinical pharmacology at the intersection of data science, patient diversity, and regulation. For the team, presenting at ACCP 2025 was not only an opportunity to share their own research but also a chance to situate their work within the broader trends shaping the field. The experience affirmed the value of their research trajectory and highlighted promising new directions for interdisciplinary and globally relevant contributions to clinical pharmacology.

This summer, CCADD welcomes five interns - Minseop Yeom, Sungwook Choi, Sunyoung Lee, Soyoung Park and Chaeyoung Chang - for the 2025 Summer Internship Program.

Minseop Yeom is a student majoring in Biotechnology and Big Data Convergence at Sangmyung University. He is particularly interested in how artificial intelligence (AI) can be applied in clinical research and used to improve the efficiency of drug development systems. Through the CCADD internship, he hopes to gain hands-on experience in data-driven approaches to clinical development. This opportunity will serve as an essential step toward his long-term goal of becoming a researcher who contributes to advancing clinical processes and drug discovery through AI-based innovation.

Sungwook Choi is a student majoring in Software Engineering and Business Administration at Konkuk University. He previously worked as a part-time employee at Celltrion, where he developed an interest in biosimilars and clinical statistics. During the upcoming summer internship program, he aims to enhance efficiency in the drug development process and contribute to the development of manufacturing and analytical methods using AI.

Sunyoung Lee is a registered nurse who earned her nursing degree from Ajou University. She gained clinical experience at Seoul National University Hospital and later worked as a Clinical Project Leader at the Development Center of Daewoong Pharmaceutical Company. With a growing interest in real-world data (RWD) and real-world evidence (RWE), she joined the CCADD internship program to further explore and develop her research competencies.

Soyoung Park is an undergraduate at Hongik University, majoring in Materials Science and Engineering with a double major in Industrial Data Engineering. At the Artificial Intelligence & Application Lab (AIA Lab), she contributed to developing machine learning models for predicting rotator cuff re-tear and reoperation. Rather than feeding MRI images directly into models, she reviewed each scan to identify the tear site and consistently measured its size. She then structured the data for modeling. By integrating these imaging features with clinical variables, she built predictive datasets and became familiar with the challenges of working with RWD, such as missing values, inconsistent ation, and the need for clinical context. This experience led to her interest in RWD analysis and how it can support data-driven decision-making in medicine. Her current interests include RWD analysis, medical AI, and applying generative AI to clinical workflows. Through the CCADD internship, she hopes to gain practical experience in clinical data analysis and build a strong foundation for future research in drug development.

Chaeyoung Chang is an undergraduate student majoring in Chemistry at Konkuk University, with a keen interest in integrating AI into drug development. She built foundational skills in data analysis through the K-Software Empowerment Bootcamp. Recently, she participated in the 2025 Google Solution Challenge, where her team developed an AI-powered emergency room matching service in the healthcare track. The team was selected among the global Top 10 and received the Most Societal Impact Award as one of the winners. She is now joining the CCADD internship to further explore AI-driven drug development and gain practical experience with real-world data.

During the 2025 summer internship, we hope interns continue to build confidence, stay curious, and actively engage in deepening their understanding of drug development and regulatory science with independence and passion.

This winter, CCADD welcomes six interns- Minsol Kim, Suhyun An, SeoYoon Jang, Seeun Park, Yura Choi and Nayoung Ku -  for the 2025 Winter Internship Program.

Minsol Kim is majoring in veterinary medicine at Seoul National University. She is interested in exploring the application of domain knowledge in the process of AI model development by utilizing her knowledge of pharmacology, toxicology, and physiology. Her goal is to build a solid foundation in AI and gain hands-on experience analyzing clinical data and investigating the drug development process. Through the 2025 CCADD winter internship program, she aims to explore potential career paths in the field of drug discovery.

Suhyun An is a student majoring in Stem Cell and Regenerative Biotechnology at Konkuk University. She previously worked as an undergraduate researcher in the Immunopharmacology Laboratory at Seoul National University College of Medicine, where she studied drug toxicity. Her current research interests include Drug-Drug Interactions (DDIs) and drug repurposing using artificial intelligence (AI). Her previous training primarily involved wet lab research. The CCADD internship will allow her to gain experience in data science applied to drug development, which will serve as a critical foundation for achieving her future research goals.

SeoYoon Jang is majoring in Systems Biotechnology at Chung-Ang University. She has a keen interest in AI and data science, which led her to complete the 2024 Boostcourse <DATA SCIENCE PROJECTS> coaching study program. Subsequently, she developed an interest in drug delivery and interned at the Lab of Drug Delivery and Cell Therapy at Sungkyunkwan University School of Medicine. Her growing passion for integrating AI into drug development led her to a winter internship at the CCADD. Through this internship, she aims to gain proficiency in data analysis tools and deepen her domain knowledge, with the goal of becoming a researcher specializing in AI-driven drug development.

Seeun Park is a senior at Yonsei University, double majoring in Biotechnology and Public Administration. She is willing to explore clinical pharmacology and regulatory science during this winter internship program. With passion in healthcare and safety, she focuses on gaining more experience and knowledge on artificial intelligence for clinical drug development and more specifically epigenetic study and policy analysis in Korea. It is her goal to work on expertise for the improvement of disease prevention and control.

Yura Choi is an undergraduate student in the Department of Chemistry at Kyung Hee University. She is passionate about AI-driven drug development, applying data science to pharmaceutical research. Through the LAIDD program, she has gained valuable insights into how AI can be applied to drug development. She aims to contribute to drug development and explore innovative therapeutic modalities by leveraging AI-driven data analysis. Currently, she is participating in the CCADD winter internship to strengthen her AI and data science expertise and deepen her understanding of research in drug development.

Nayoung Ku is an undergraduate in life science and AI convergence at Handong Global University. She previously earned a Bachelor’s degree in psychology at the National Institute of Lifelong Education by Examination for Self-Education. Nowadays, Nayoung mainly develops prediction modelling and optimization algorithms with various datasets. She had been a research assistant at Biodata Laboratory, Handong Global University, focusing on cancer diagnostic machine learning models using tumour-educated platelet (TEP) RNA-seq data for a year. Recently, she did a capstone project on NLP for an ovarian cancer diagnostic model using TEP RNA-seq pre-processed data, which showed her creativity in analytic approach. She received the Daewoong Pharmaceutical CEO Award at the JUMP AI 2024 Drug Discovery Competition, sponsored by the Korea Pharmaceutical and Bio-Pharma Manufacturers Association. In the competition, Nayoung and her team developed an advanced multi- perceptron to predict IC50 values for IRAK4 inhibitors by applying novel and strategic approaches to accelerate drug discovery processes. With her enthusiasm for enhancing the efficiency of drug discovery, she participates in this internship to experience real-world clinical data and its analysis, like electronic medical records. With a heart of admiration, she is yearning to learn the extraordinary methodology of CCADD. 

We hope that during their winter internship, they will build confidence in their area of expertise, develop independent learning skills to explore new knowledge, and dedicate their enthusiasm to gaining a comprehensive understanding of drug development and regulatory science throughout these two months.

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.

Prof. Howard Lee spoke at a conference held by The Korean Society of Food, Drug and Cosmetics Regulatory Sciences, December 13, 2024, in the Hoam Faculty Club, Seoul National University. Prof. Lee discussed the current status and future perspectives as to the application of artificial intelligence technologies to clinical drug development. A newspaper article focused on the point that Prof. Lee emphasized at the Q&A session, where Prof. Lee stressed the establishment of the standards in order for AI to be more broadly accepted into the real-world practices of drug development, particularly for the clinical stage including post-marketing approval.