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- kidney disease prediction
- digital medicine
- machine learning in nephrology
- Interoperability
- deep learning in medical imaging
- unintended consequences
- ai-powered diagnosis
- national healthcare insurance
- three lows framework
- chronic kidney disease (ckd)
- mutimodal ai
- legal responsiblity
- ai-based risk stratification
- healthcare policy
- medical reform
- himss
- essential medical care
- retinal imaging for ckd
- ccmm
- acute care patient discharge support and community linkage pilot project
- ai in healthcare
- system reform
- krcdi
- gmai
- on-promise
- krcore
- continuity of care
- early detection of ckd
- tertiary hospital structural reform pilot program
- patient's safety
- Today
- Total
목록2025/03 (4)
Connected Care
1. The Importance of Early CKD DetectionChronic kidney disease (CKD) is a major global health challenge, with most patients diagnosed only after losing more than half of their kidney function. Late detection limits opportunities for early intervention, increasing the likelihood of complications such as kidney failure and cardiovascular disease. To mitigate these risks, early diagnosis and risk s..
Medical artificial intelligence (AI) has made remarkable strides, from detecting pneumonia on chest X-rays to predicting sepsis risk in critically ill patients. However, existing AI models remain constrained by their narrow task scope and reliance on single-modal data. Most systems require retraining for each new function and struggle to synthesize diverse clinical information, such as imaging, ..
The Electronic Medical Record (EMR) system has become an essential tool in modern healthcare. However, due to inconsistent formats and a lack of interoperability, hospitals struggle to exchange medical data efficiently. As a result, patients must manually transfer their records when changing hospitals, leading to duplicate tests, unnecessary costs, and delays in treatment. These inefficiencies u..
Digital healthcare is rapidly evolving, with AI and big data playing an increasingly crucial role in patient care. However, it’s not always easy to see where these innovations are happening or how they are being applied in real-world settings.That’s why I found this recent study particularly interesting. It’s not just another theoretical AI project—it’s a study conducted by a Korean research tea..