case patient treatment patients multiple

The gap

Supporting evidence — 2 representative gaps

• Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions (2025) · doi

  8.1 Emerging trends in cardiovascular imaging Healthcare is becoming more personalized and precise due to the development of AI technologies and their integration into various fields of medicine, such as cardiovascular imaging. This will enhance patient outcomes by providing early diagnosis and prompt management of CVD120. Although AI models have numerous advantages, some limitations prevent them from being widely used in medicine. One of them is that most of the AI models are trained on data that is small, narrow, and not diverse. This could result in lower generalizability and over-fitting. To overcome this issue, Federated Learning (FL) or Transfer Learning methods can be used, which train the models with data from different sites without centralising it. This leads to a decreased risk of data leaks or unauthorized access as the data is not sent over to other networks121,122. Due to training with multiple datasets, the model becomes familiar with rare cases, which increases its sensitivity and results in decisions that are less biased. The performance of such models is similar to those trained on centrally hosted datasets and much better when compared to models trained with data from a single institution, according to the latest studies123. Another disadvantage of AI models is their “unexplainable” feature, also known as “black- boxes”. This means that how the AI reaches a specific conclusion or diagnosis is not known. The lack of transparency its working124,125. Explainable AI methods are being developed to resolve this issue126. Class Activation Mapping (CAM) is a technique that comes under Explainable AI. CAM can identify the input patterns within the deep neural network, which leads to the activation of certain outputs and shows the findings on the image. This helps the physicians who use AI to better understand the reasoning of the model and make informed decisions127. AI can also serve as a means of bringing together different imaging modalities, which can be extremely helpful, especially in the diagnosis of heterogeneous regarding questions raises diseases, for example, heart failure and atrial fibrillation128. The multimodality AI approach combines information, in the form of image, text, audio, video, and language, obtained from different imaging methods. Each modality offers important additional data, resulting in accurate outputs. A study was done to distinguish between the causes of left ventricular hypertrophy by merging data from ECG and echocardiography. The multimodal AI was found to have higher sensitivity and specificity compared to physicians129. Therefore, as AI technologies are evolving rapidly, it is changing how cardiovascular imaging is used, by improving image quality, reducing the time and workforce required for image analysis, and providing prognostic information10,130. 8.2 Wearable AI Devices timely detection ECG is an essential and most frequently used non- invasive test in cardiology. However, to record and interpret an ECG, the individual is required to visit a healthcare professional. This results in paroxysmal arrhythmias being undetected. The integration of ECG monitoring and AI-based ECG interpretation into wearable devices like smartwatches has led to widespread access to ECG131. Furthermore, there has been an increase in the detection of arrhythmias131,132. In case of AF, early detection is crucial to avoid progression and development of complications such as stroke. As most cases have either nonspecific symptoms or are asymptomatic, patients are unaware of their condition and hence do not seek medical care. Using wearable devices with AI technology, early and in improved treatment outcomes133. Apart from arrhythmias, such devices can also be helpful in the monitoring of patients with other CVDs, for example, CHD, Left Ventricular Systolic

  Keywords: models imaging used image devices cardiovascular early diagnosis trained different wearable detection arrhythmias healthcare development
• Integrative Management of Chronic Cerebral Venous Sinus Thrombosis with Ayurveda and Physiotherapy: A Case Report (2026) · doi

  This case report is limited to a single patient, lacking comparative data on treatment outcomes across multiple patients with chronic CVST.

  Keywords: case report limited single patient lacking comparative treatment outcomes across multiple patients chronic cvst