Background

The development of artificial intelligence (AI) in the healthcare sector has opened significant opportunities to enhance the accuracy of diagnosis and therapy effectiveness. One rapidly growing approach is multimodal deep learning, which integrates various data types to support more accurate clinical decision-making. In the medical field, the combination of facial image data and medical records has great potential to assist in predicting a patient’s therapy needs more personally and precisely.

Facial images can provide physiological and emotional information related to a patient's health condition, such as signs of fatigue, pain, or changes in expression due to certain diseases. Meanwhile, medical records contain health history, examination results, and clinical parameters that can be used to objectively evaluate therapy needs. By combining these two data sources through a multimodal deep learning model, it is expected that more accurate therapy quantity predictions can be achieved compared to conventional methods that rely solely on a single data type.

Currently, many therapy prediction approaches still depend on manual analysis by doctors based on medical records and patient interviews, which can be subjective and time-consuming. The use of deep learning enables automated analysis with high accuracy by extracting features from facial images and processing medical record data. The integration of these two data types through multimodal fusion techniques can enhance the understanding of a patient's condition in a more comprehensive and efficient manner. This system is expected to enable faster, more accurate, and data-driven therapy decision-making, ultimately improving treatment effectiveness and the quality of life for patients.

Research Objective

To explore and analyze non-linear relationships between patient facial image data, medical records, and therapy quantity based on therapy progression using deep learning.

Expected Results

This research is expected to demonstrate that Face Analyzer based on multimodal deep learning can predict therapy quantity more accurately, quickly, and objectively compared to conventional methods. By integrating facial image data and medical records, this system can help doctors reduce subjectivity and accelerate therapy decision-making, thereby increasing the efficiency of healthcare services. If successful, this technology could serve as a foundation for AI-based therapy prediction and personalization systems, ultimately contributing to improving treatment effectiveness and enhancing patient quality of life.