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.

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.