2024년 6월 1일(토) 사전 워크숍에서 정해진 주제는 아래와 같으며, 6월 22일(토) 주제 피칭 전까지 변경될 수 있습니다.
| Team | 리더 | 데이터셋 | 주제 |
| A | 리더: 권OO | K-MIMIC & MIMIC-IV | Investigating Bias in Machine Learning Models: Applying MIMIC data to Korean-MIMIC data |
| B | 리더: 김OO | INSPIRE | Discovery of risk factors influencing postoperative mortality using INSPIRE dataset |
| C | 리더: 김OO | INSPIRE | Differences in treatment and perioperative complications according to the characteristics of departments, emergency surgery status, healthcare reimbursement systems, or patient groups at Seoul National University Hospital |
| D | 리더: 김OO | K-MIMIC | Prediction Model for Neonatal (or Adult) Acute Kidney Injury Adjusted for Regional Characteristics: A Retrospective Cohort Study of the K-MIMIC Database |
| E | 리더: 백OO | K-MIMIC & MIMIC-IV | Domain analysis between K-MIMIC and MIMIC-IV |
| F | 리더: 이OO | K-MIMIC | Detecting institution-dependent covariate shift |
| G | 리더: 장OO | KMLE | Comparison of ChatGPT 4o's Performance in KMLE and USMLE and Creating a KMLE Question Bank Using ChatGPT 4o |
| H | 리더: 정OO | INSPIRE | Screening latent perioperative complications from preoperative and intraoperative characteristics |
| I | 리더: 정OO | K-MIMIC | Short-term ICU mortality prediction using real-time time series: alarm system |
| J | 리더: 한OO | KMLE | Evaluating the performance of GPT-4o on Korean Medical Licensing Examinations using both text and image data |
