# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 5 Challenges and Future Recommendations  
The advancements in artificial intelligence are allowing researchers the use of ECG data, whether the format is tabular, waveform, image or video, etc., for the diagnosis of different health issues along with cardiac problems. Although the performance is presentable during the experiment for those studies, accuracy for a different dataset for which the model has not been trained and reliability remains questionable. This section discusses the current challenges, limitations, and probable future research directions. * _External evaluation/standard system for evaluation_: The first limitation of the current studies is an external evaluation or a standard system for evaluating the performance of the models produced by the researcher to create an acceptable benchmark. Also, when the disease differs between race and geographical area, the models do not have any answer or options for modification for those terms. * _Ability to be applied in clinical setups_: Despite excellent experiment performance, none of the algorithms made it to a medical setup. The main reason behind this conclusion is that the use of ECG in most cases cannot be the only data source to achieve a conclusive diagnosis. Also, medical professionals are less likely to accept it for diagnosis unless the model's performance is consistent over a different dataset with different characteristics and there is no regulatory problem. * _Limited resource in wearables for computation_: The use of deep learning techniques in wearables, which use substantial neural networks, has another disadvantage. Significant computational resources are needed to handle and evaluate the data from these networks. This can be difficult for wearable technology or other edge computing devices since they do not have the capabilities to carry out such computationally demanding activities locally. In these circumstances, relying on cloud-based programs or distant servers to process data and make decisions can be necessary. This drawback emphasizes how crucial it is to consider the computing capabilities and resource needs when developing deep learning algorithms for ECG analysis in wearable technology or other situations with limited resources. Also, at the same time, using the cloud as an option to take care of the computation question comes to form the front of privacy and security. Sending personal information over the cloud makes it vulnerable to attacks or leaks. Therefore, the infrastructure needs to be there with proper security and privacy protocol before the data can be moved to another location for analysis or diagnosis. * _Imbalanced and limited labeled datasets_: Imbalanced datasets are a big concern. Training deep learning algorithms requires significantly large amounts of data. However, not all cases or classes of diseases or health conditions have a large dataset with a balanced format. Therefore, the unavailability of a balanced dataset may provide accurate results, but that cannot be taken as a general rule and can result in overfitting or underfitting. * _Interpretability and transparency_: In utilizing deep learning algorithms for ECG analysis to diagnose disorders, interpretability and transparency are significant problems. Convolutional neural networks (CNNs) are an example of a sophisticated deep learning model that is sometimes called a \"black box\" since it lacks explicit rules or explanations that humans can understand. It raises questions about the validity and dependability of the diagnostic judgments made by these models due to this lack of interpretability. Clinicians in conventional medicine rely on interpretability to comprehend the under lying causes of a diagnosis. They must be able to describe how specific characteristics or patterns in the ECG signal help to identify a particular condition. To track down and comprehend the precise elements or patterns that lead to a given diagnosis, deep learning models function by learning sophisticated representations from enormous volumes of data.