Annotated Bibliography The Role of AI in Disease Prediction and Management

Badidi, E. (2023). Edge AI for early detection of chronic diseases and the spread of infectious diseases: Opportunities, challenges, and future directions. Future Internet, 15(11), 370. https://doi.org/10.3390/fi15110370

Badidi (2023) discusses edge AI in the context of predictive healthcare since it effectively traces the emergence of chronic illnesses. Edge AI operates within the network’s edge, providing low latency and real-time decision-making. This approach employs detailed algorithms in machine learning and deep learning over significant data from electronic health records and wearable devices. Such innovations call for early prevention of health conditions that may worsen if left unaddressed for long. Furthermore, federated learning is quite effective in storing data on individual devices and improving the centralized model without compromising privacy. The article describes opportunities of Edge AI in healthcare for diagnosis enhancement and patient data safeguarding. Other essential aspects covered include how to include more technologies in each stage of the clinical process, how to handle data, and how accurate an AI prediction is. The source is valuable in understanding the possible use of AI technologies in managing or preventing chronic diseases

Sun, X., Yin, Y., Yang, Q., & Huo, T. (2023). Artificial intelligence in cardiovascular diseases: Diagnostic and therapeutic perspectives. European Journal of Medical Research, 28(1). https://doi.org/10.1186/s40001-023-01065-y

The research by Sun et al. (2023) focuses on the use of Artificial Intelligence AI in diagnosing and treating cardiovascular diseases or CVDs. The study underscores that AI can improve the tools and methods for the analysis and the concepts of therapeutic management. The authors also highlight the AI prospect with heart failure, atrial fibrillation, and other CVDs diagnostics with a rich and complex data analysis that is superior to standard methods. It is more effective in using swift and accurate diagnostics in patients by integrating the applications of AI into the echocardiogram and electrocardiogram. It assists clinicians in arriving at better treatment plans since they use better prediction tools. This source is beneficial in explaining how AI applications foster innovation in predictive healthcare. It advocates applying these technologies to manage chronic diseases like CVDs to avert adverse issues and enhance patient outcomes.

Rashid, J., Saba Batool, Kim, J., Muhammad Wasif Nisar, Hussain, A., Juneja, S., & Riti Kushwaha. (2022). An augmented artificial intelligence approach for chronic disease prediction. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.860396

This research aims to investigate an enhanced artificial intelligence model using artificial neural models augmented by particle swarm optimization algorithms to predict chronic diseases. In this study, Rashid et al. (2022) introduced a new approach that indicates how to enhance the efficiency of chronic disease prediction models regarding feature selection using PSO because big data situations in healthcare environments are usually challenging for health IT solutions. The research demonstrates that the proposed method has the potential to predict diabetes, heart diseases, and cancer with a high accuracy, which highlights its importance in the timely diagnosis and prevention of chronic diseases. In this regard, it can be concluded that AI use in predictive health care enhances the capacity for early management techniques, hence eliminating the progression of chronic diseases. This research is vital in examining how new AI technologies can be applied to improve healthcare outcomes; the positive impacts of AI on the diagnosis and treatment of chronic illnesses are shown in this study.

Zhu, Z., Zhao, S., Li, J., Wang, Y., Xu, L., Jia, Y., Li, Z., Li, W., Chen, G., & Wu, X. (2024). Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease. Respiratory Research, 25(167). https://doi.org/10.1186/s12931-024-02793-3

Zhu et al. (2024) introduced a new efficient diagnostic model for the early identification of Chronic Obstructive Pulmonary Disease (COPD) using deep learning and epidemiology with radiomics characteristics. The comprehensive model employed used the Multi-Layer Perceptron (MLP) and Computed Tomography images and patient questionnaire data, resulting in a high AUC value of 0. 971. This model makes the COPD diagnosis more effective than the previously used ones, indicating how AI can positively affect disease prediction and management. From this source, it is possible to learn how the combination of different types of data can improve the diagnostic productivity of healthcare institutions, especially where the disease has factors of variability, such as in COPD.

Cai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Li-Ling, J., Hu, W., Yin, Z., Gong, D.-X., & Zhang, G.-W. (2024). Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Medicine, 22(56). https://doi.org/10.1186/s12916-024-03273-7

The systematic review focused on the implementation and efficacy of AI in risk-predictive models for cardiovascular disease (CVD). Cai et al. (2024) review 486 AI models and discuss the significant shortcomings of the external validity and the risks of obtained biases regarding these prediction tools. To quantify the reliability and replicability of these models, the study suggests another indicator: The Independent Validation Score. The study also reveals inadequate development of the AI models, the statistical ones, and the external validation procedures. It pointed out how AI improves risk prediction of CVD but showed the risk of deploying it in clinical applications without external validation. This source is helpful because it presents a conceptual model and process to increase the effectiveness and accuracy of the AI model before implementation in the medical field.

Singh, V.; Asari, V.K.; Rajasekaran, R. (2022). A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease. Diagnostics, 12, 116. https://doi.org/10.3390/diagnostics12010116

Singh et al. (2022) focus on developing a deep neural network to diagnose and forecast Chronic Kidney Disease. The study also validates that Recursive Feature Elimination (RFE) improves the deep learning model's outcome of crucial CKD diagnosis features, including hemoglobin and serum creatinine. This deep learning technique has become more effective than conventional machine learning algorithms such as Support Vector Machine (SVM) and logistic regression. For instance, the distress model was identified to provide 100% classification in tests, contributing to better diagnostics in the medical field. The success of the deep learning algorithm in predicting CKD that has been determined in this study demonstrates how imperative the deep learning model is in enhancing the early diagnosis and management of CKD.