Development of AI-based Nursing Record Generation and Task Automation Technology
Nursing records are essential to patient-centered care, supporting clinical decision making, communication, and legal documentation. However, nursing records remain time consuming and error. This project introduces an AI driven system leveraging a large language model (LLM) to automate nursing records and improve workflow efficiency through AI agent technologies.
The system integrates a HIS with a cloud-based, healthcare-specialized LLM platform. Patient data text, speech (via STT), and images are standardized in EMR format within the HIS and securely transmitted to the cloud. After preprocessing , data are processed by the LLM, and results are returned to the HIS for clinical use.
The platform supports a wide spectrum of nursing records, from admission/discharge summaries and perioperative notes to real time logs like medication administration and progress notes. All outputs are mapped to international medical terminology standards, and the solution is provided as a platform independent, cloud-based SaaS, enabling scalability and flexibility
By reducing repetitive documentation tasks, the system allows nurses to focus more on patient care, while enhancing documentation accuracy, completeness, and timeliness. LLM-based validation further minimizes errors and omissions, improving the overall quality and safety of clinical practice.
AI-based Early Detection and Prognostic Prediction for Autism Spectrum Disorder (ASD)
Background and Necessity
The global prevalence of ASD is rising, yet specialist shortages delay diagnosis and intervention.
Early intervention improves outcomes, underscoring the need for digital medical devices for early ASD detection.
Project Objectives
Collect longitudinal developmental data from infants and toddlers at risk for ASD.
Extract early risk factors from newborn cohort data and develop AI algorithms for video-based early sign detection.
Develop multimodal AI models using voice-to-text analysis to predict ASD progression and obtain TTA certification.
Expected Outcomes
Support personalized ASD diagnosis based on individual behavioral and developmental data.
Identify ASD risk and protective factors specific to the Korean population through large-scale cohort analysis, promoting early intervention.
