이현훈 교수

Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence and poorly calibrated uncertainty. We define medical hallucination as any model-generated output that is factually incorrect, logically inconsistent, or unsupported by authoritative clinical evidence in ways that could alter clinical decisions. We evaluated 11 foundation models (7 general-purpose, 4 medical-specialized)

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosti

We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs

Background The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients’ symptoms and recommends the appropriate medical specialty could provide a valuable solution. Objective In order to establish a contactless method

Bee venom (BV) is one of the alternative medicines that have been widely used in the treatment of chronic inflammatory diseases. We previously demonstrated that BV induces immune tolerance by increasing the population of regulatory T cells (Tregs) in immune disorders. However, the major component and how it regulates the immune response have not been elucidated. We investigated whether bee venom phospholipase A2 (bvPLA2) exerts protective effects that are mediated via Tregs in OVA-induced asthma

O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the

BACKGROUND: Stemona tuberosa has long been used in Korean and Chinese medicine to ameliorate various lung diseases such as pneumonia and bronchitis. However, it has not yet been proven that Stemona tuberosa has positive effects on lung inflammation. METHODS: Stemona tuberosa extract (ST) was orally administered to C57BL/6 mice 2 hr before exposure to CS for 2 weeks. Twenty-four hours after the last CS exposure, mice were sacrificed to investigate the changes in the expression of cytokines such a

BACKGROUND: ChatGPT (Open AI) is a state-of-the-art artificial intelligence model with potential applications in the medical fields of clinical practice, research, and education. OBJECTIVE: This study aimed to evaluate the potential of ChatGPT as an educational tool in college acupuncture programs, focusing on its ability to support students in learning acupuncture point selection, treatment planning, and decision-making. METHODS: We collected case studies published in Acupuncture in Medicine be

Ventilation should be assisted without asynchrony or cardiorespiratory instability during anesthesia emergence until sufficient spontaneous ventilation is recovered. In this multicenter cohort study, we develop and validate a reinforcement learning-based Artificial Intelligence model for Ventilation control during Emergence (AIVE) from general anesthesia. Ventilatory and hemodynamic parameters from 14,306 surgical cases at an academic hospital between 2016 and 2019 are used for training and inte

The American Society of Anesthesiologist's Physical Status (ASA-PS) classification system assesses comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language processing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summaries, comparing their performance to human physicians. Data from 717,389 surgical cases in a tertiary hospital (October 2004-May 2023) was split into training, tunin

Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and extern

Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k -means c

BACKGROUND: Major adverse cardiovascular and cerebrovascular events (MACCEs) after noncardiac surgery can lead to substantial morbidity, mortality, and health care costs. Therefore, accurate and rapid risk prediction is crucial for targeted perioperative management. This study aimed to develop and validate a minimally burdensome multimodal deep learning model integrating demographic data, the International Classification of Diseases (ICD)-10 procedure codes, and raw preoperative 12-lead electroc