Seeun Park is currently an undergraduate researcher at CCADD. She became an undergraduate student researcher after successfully completing the 2024 Winter Internship program. She is a Bioengineering major 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.

This winter, CCADD welcomes six interns - Minsol Kim, Suhyun An, Seo-Yoon 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.

Seo-Yoon 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.

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.

This summer, CCADD welcomes three interns-Kim Chanmin, Choi Yunjin, and Yoo Kyungwan -for the 2024 Summer Internship Program.

Kyungwan Yoo, a student at Kyung Hee University, earned his Bachelor’s degree in genetic engineering. He worked as an undergraduate researcher at the Kyung Hee University Regenerative Medicine Institute, focusing on creating cell-targeting treatments. Currently, he is interested in integrating biological science with artificial intelligence (AI) technology and applying it to drug development. Kyungwan applied for the 2024 CCADD Summer Internship Program to expand his knowledge of drug development in general, be it clinical trials, and/or utilizing medical big data. He also intends to gain practical experiences in applying programming languages to these domains.

Yunjin Choi has graduated from Stony Brook University with a Bachelor’s degree in mathematics. After graduating, Yunjin worked as a statistical analyst at the Biomedical Research Institute, Korea University Guro Hospital. She has mainly conducted retrospective studies on nephrology and neurology. For example, she published a paper entitled "Fasting glucose variability and risk of dementia in Parkinson's disease: a 9-year longitudinal follow-up study of a nationwide cohort", which examined the impact of fasting glucose variability on the risk of developing dementia in Parkinson's disease patients over a nine-year period. The study found that greater variability in fasting glucose levels was associated with a higher risk of dementia, further suggesting that stabilizing glucose levels might be beneficial for cognitive health in these patients. Besides being the first author of the study, she was also responsible for data analysis and interpretation, ensuring the accuracy and reliability of the study results. She also has the opportunity to learn how to draft and revise the manu and enhance its scientific merit. During this time, Yunjin developed an interest in Real-World Data (RWD), such as electronic medical records (EMR). She intends to pursue her interest while at CCADD.

Chanmin Kim is a Biological Engineering major at Kangwon National University. She became interested in developing new drugs while learning drug delivery systems. She also completed GMP training. After receiving the Excellence Award for designing smart healthcare products at Gachon University's DNA (Data, Network, AI) School Camp , she became interested in improving the efficiency of developing new drugs by applying data science and AI. Her goal is to become a researcher in the development of new drugs using AI. Through her 2024 CCADD summer internship program, she will pursue this goal.

We hope that during their summer internship, they will gain confidence in their field of study, learn how to study independently and explore new knowledge, and devote all their passion to gaining a deep understanding of drug development and regulatory science over the course of these two months.

CCADD attended the 2023 Fall Conference of the Korean Society of Medical Informatics (KoSMI) held at HIP(Healthcare Innovation Park) of Seoul National University Bundang Hospital, South Korea. The conference spanned three days from November 29th to December 1^st, both online and offline. Aligned with the theme "Beyond Data: Actionable Health AI", the conference featured 27 symposiums covering various topics, from artificial intelligence in the healthcare to standardization of medical data and digital health.

The first day was conducted online. Particularly, ”Symposium 6: Development of Predictive Models for Cardiovascular Diseases Using Medical Big Data” was noteworthy, where Prof. Howard Lee served as the session chair and speaker, attracting significant attention from the audience with the presentation on ”Improving the Performance of Machine Learning Models for Predicting Acute Coronary Syndrome in Type 2 Diabetes Patients through Substitution of Propensity Scores for Binary Variables”. Also, throughout the conference, the poster presentation was led by David Seung U Lee.

On the second day, each symposium delved into topics of medical data and artificial intelligence applications to the healthcare domain. Themes included "Strategies for the Activation and Commercialization of AI-Based Drug Development" and "Smart Curation of Medical Data: Approaches for Preparing for the Utilization of Medical AI." This encompassed insights into the industry's latest strategies for drug target exploration. In Symposium 9, Dr. Siun Kim delivered a presentation on ”Addressing the challenges of data scarcity and bias of AI-based drug development”. The discourse extensively covered specific manifestations of data scarcity and proposed strategies for mitigation within drug development.

During the final day, the symposiums provided an opportunity to approach the trends in the utilization and data processing methods of developing LLM. Participants delved into the practical approaches for utilizing Real-World Data (RWD) in regulatory decision-making, replicating Randomized Controlled Trials (RCT), and employing common data models. CCADD members exchanged ideas with fellow researchers studying similar research topics, offering a valuable opportunity to comprehend ongoing research trends. It was an insightful symposium for healthcare information researchers to share and discuss the latest trends in research.

"Artificial Intelligence (AI) has a bright future in the area of drug discovery and development", Prof. Howard Lee agreed. But, he went on saying, "The focus has been drug discovery, particularly de novo search of new 'drug-like' molecules, which came up with a mix of some successes and mostly failures." To Prof. Lee, there is a more important area where AI applications have not been tried much, and that is clinical development, more expensive, lengthy, and riskier. These statements were made at a K-Healthcare Business Forum, co-sponsored by Young Doctors, KBR, and KHIDI, on October 13, 2023.

In a speech entitled "AI-based drug development: Hype or Reality?", Prof. Lee explained why the application of AI technologies has been mostly limited to early stage or drug discovery, and how this can be expanded to the later phase of drug development such as preclinical toxicity prediction and more robust and efficient design and conduct of clinical trials. The details of Prof. Lee's talk can be found in this news paper article. 

CCADD was honored to participate in the joint symposium organized by Seoul National University (SNU) and Kyushu University (KYU) on September 14th. This marks the fourth year of the annual symposium, which was established to foster research collaboration between SNU and KYU.

This year's symposium focused on the theme of “Navigating the Artificial Intelligence (AI) Frontier: A Collaboration.” The discussions were categorized under three sub-themes:

AI for a Sustainable Future: Advancements and Challenges

AI and Human-Centric Innovation: Shaping the Future Together

AI Ethics and Responsible Innovation: Shaping a Trustworthy Future

Under the second sub-theme, David Seung U Lee represented CCADD and presented on “Predicting the efficacy of investigational drugs using real-world data.” This sparked subsequent discussions exploring avenues for further collaboration among the participants.

 In a gathering of brilliant minds from around the world, Professor Howard and his team attended Conference on Health, Inference, and Learning (CHIL) 2023. The three-day event, held from June 22nd to 24th in Cambridge, Massachusetts, provided a platform for cutting-edge research and discussions on the intersection of artificial intelligence (AI) and healthcare.

Prof. Howard, along with his students Jung Hyun and David, as well as his former student, Siun, were among the select few from South Korea to be invited to this renowned conference. Their attendance was further bolstered by the acceptance of their research paper, titled "Revisiting Machine-Learning Based Drug Repurposing: Drug Indications Are Not a Right Prediction Target," which they presented during the first day's poster session. The team explained the significance and implications of their study, actively engaging with fellow participants and answering questions. More information about the paper can be found here.

The conference encompassed an extensive array of topics, all of which were essential for researchers navigating the rapidly evolving field of AI in healthcare. Attendees were treated to enlightening talks and exciting ideas that spanned innovation, equity, and generalizability in machine learning for health. Discussions included "innovation in machine learning for health," "sharing health data in an age of generative AI: risks, limitations and solutions," "equity implications of machine learning in health: how technology could help or hurt," and "generalizability in machine learning for health: critical for robustness or a distraction from specific validation."

Every moment of the conference proved to be an eye-opening experience. It served as an extraordinary opportunity to broaden their perspectives, push boundaries, and venture beyond their comfort zones.