Overview
This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.
Description
Cancer diagnosis and early detection are crucial for improving patient outcomes and survival rates. Early identification of cancers and appropriate intervention can significantly impact treatment success and prognosis. In clinical practice, oncologists often need to integrate a variety of patient data, including medical history, laboratory test results, imaging data such as CT scans and MRIs, and genetic markers, to make an accurate diagnosis and develop a personalized treatment plan. As precision medicine becomes increasingly important, the challenge remains to identify cancer at early stages, especially when symptoms are subtle or absent. Recent advancements in artificial intelligence and data analysis techniques have shown great promise in enhancing the diagnostic accuracy and speed of cancer detection. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, imaging, laboratory results, and genetic data. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized treatment options for cancer patients. Ultimately, this system seeks to improve early detection, guide effective treatment strategies, and enhance patient survival rates.
Eligibility
Inclusion Criteria:
1、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available).
2. Individuals without severe cognitive impairments or conditions that would prevent
them from providing informed consent or participating in the study.
3. Parents or guardians must provide informed consent for minors, while adult
participants must provide informed consent for themselves.
Exclusion Criteria:
- Patients with incomplete or missing key electronic health record data or insufficient follow-up data.
- Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.
- Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).