Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to pr

The gap

Consensus across the literature

Supporting evidence — 6 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
• Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions (2025) · doi

  ● Generalizability to the population as a whole: How can federated and transfer learning be utilized to achieve robust performance for individuals in a range of ethnic, age, and comorbid disease groups without compromising data sharing121-123? ● Trade-off between explainability and performance: How much transparency of the model (XAI techniques, CAM maps) is needed regulatory agency for clinician approval, and what implications does this have for clinical accuracy diagnostic implementation? trust and and www.discoveriesjournals.org/discoveries 19 AI in Cardiovascular Imaging: State and Outlook ● Integrating into the clinical workflow: What are the optimal AI-human interaction paradigms (e.g., real-time triage, HITL oversight, automated reporting) to maximize efficiency gains without trading off safety in high-acuity settings? ● Regulation and liability: What processes should be used to “oversee” dynamic “learning” algorithms in operation, to handle degradation, to ensure patient safety, and to apportion liability among and practitioners? the developers, organisations, economic ● Cost-effectiveness and access: What will be the of AI sustained implementation on health-system costs, and how can AI solutions be scaled resource- constrained settings to mitigate inequalities? implications to

  Keywords: learning performance without implications clinical implementation safety settings liability generalizability population whole federated transfer utilized
• THE IMPACT OF ARTIFICIAL INTELLIGENCE IN MEDICAL BIOSTATISTICAL STUDIES: ARE THE STATISTICIANS NO LONGER NECESSARY? A LITERATURE REVIEW (2025) · doi

  New AI technologies in medical biostatistics include biostatistics New AI technologies in medicine and federated medical learning, generative AI, and explainable AI (XAI). Such technologies allow advanced analysis of distributed data without sacrificing privacy, explainable prediction and decision-making models, as well as longitudinal biostatistical modeling. They address to specific issues such as data heterogeneity, high dimensionality and model interpretability for advanced biostatistical analysis [55]. facilitating more Federated learning is based on machine learning algorithms and data collection at a centralized storage node to allow fast and accurate diagnosis and treatment. It was investigated in a healthcare surveillance system (Internet of Medical Things- IoMT) based on blockchain powered by federated learning [56]. Models of generative AI such as GANs, VAEs, etc., are used to learn the distribution of complex datasets and to generate synthetic biomedical data that resembles authentic samples [57]. Generative AI can generate realistic patient records and/or medical images which can help to prevent certain challenges such as limited sample sizes and imbalance of the clinical datasets. Generative AI also enables simulation studies, privacy- preserving data sharing, and hypothesis testing through the generation of controlled synthetic cohorts. In biostatistical modeling, these tools help make models more robust, enhance generalizability of predictive models, and improve information sharing across models with richer, multi-modal data [58]. Explainable AI (XAI) aims to provide humans with transparent, interpretable, and understandable AI models, one of the most

  Keywords: models medical learning generative technologies federated explainable biostatistical biostatistics allow advanced privacy modeling based datasets
• From prediction to practice: artificial intelligence as an enabling layer in liver transplant care – reflections on ILTS 2025 (2026) · doi

  Near-term (≤ 1 year) The most immediate priority is the creation of standardized, multi-center data commons that pool heterogeneous transplant datasets while preserving patient privacy through federated learning techniques. By training models on a broader spectrum of practice patterns and patient demographics, we can improve

  Keywords: patient near term year immediate priority creation standardized multi center commons pool heterogeneous transplant datasets
• Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems (2026) · doi

  Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to produce trustworthy and locally deployable machine learning and artificial intelligence (ML/AI) models remains uncertain.

  Keywords: health learning background national electronic record networks support systems lhss enabling large scale aggregation monitoring
• The Role of Artificial Intelligence in Early Disease Detection Techniques, Applications, Challenges and Future Directions (2026) · doi

  Ciona Dewan1, Raghu Raja Mehra2 1Invictus International School, Amritsar Email: cionadewan025[at]gmail.com 2Invictus International School, Amritsar Email: raghu[at]invictusschool.edu.in Abstract: Early and accurate detection of disease is one of the most decisive factors in patient survival, treatment cost and quality of life. Artificial intelligence (AI), and in particular machine learning and deep learning, has emerged as a powerful ally in this effort, capable of analysing medical images, electronic health records, laboratory results and wearable-sensor data with remarkable speed and consistency. This paper reviews the role of AI in early disease detection, surveying the principal techniques, the typical detection pipeline, and applications across cancer, cardiovascular, ophthalmic and neurological disorders. A comparison with conventional diagnostic methods shows that AI systems can match or exceed clinician-level accuracy in several screening tasks while operating at scale. The paper then proposes an integrated, privacy-preserving and explainable framework for clinical deployment, and critically examines the advantages, limitations, and ethical and regulatory challenges involved. Finally, it outlines future directions—including federated learning, explainable AI and continuous wearable monitoring—that could make trustworthy, equitable early detection a routine part of care. Keywords: artificial intelligence, machine learning, deep learning, early disease detection, medical imaging, screening, explainable AI, healthcare

  Keywords: detection learning early disease explainable raghu invictus international school amritsar email artificial intelligence machine deep