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All Journal Journal of Social Research Jurihum
Arbaz Haider Khan
University of Punjab
Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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AI-POWERED HEART FAILURE PREDICTION AND MONITORING TOOLS Roman Khan; Arbaz Haider Khan; Hira Zainab; Hafiz Khawar Hussain
JURIHUM : Jurnal Inovasi dan Humaniora Vol. 2 No. 3 (2024): JURIHUM : Jurnal Inovasi dan Humaniora
Publisher : CV. Shofanah Media Berkah

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Abstract

Recently, a chronic and severe form of cardiovascular diseases – heart failure (HF) – became preventable with the aid of artificial intelligence (AI). In this article, we explore the multiple ways in which AI is employed to enhance the care of patients with heart failure: remote real-time supervision systems, individualized interventions, risk assessment models. AI’s ability to review massive amounts of data from Wearables, electronic health, and record checking tools may aid heart failure early detection, risk elevation, and preventive treatments. This enhances the patients’ quality of life, and also reduces the client’s expenditure on healthcare. Several challenges remain relating to: AI availability and data quality; algorithm explain ability; legal and regulatory aspects; and patient engagement, even if there are positive preliminary signs for the broad development of AI-based solutions in the health field. Even bigger promises for the improvement of precision and individualized heart failure therapy are seen in future developments of AI through application of big data, genomics, and remote touchscreen monitors. The work on the improvement of the explainable AI models and expanded international cooperation will also help solve these problems and enhance the efficiency as well as equity of heart failure treatment. With rapid advancements in Artificial Intelligence, it is expected that the care of patients with heart failure will be transformed, both in terms of time, efficiency, and individual patient needs.
Deep Learning in the Diagnosis and Management of Arrhythmias Arbaz Haider Khan; Hira Zainab; Roman Khan; Hafiz Khawar Hussain
Journal of Social Research Vol. 4 No. 1 (2024): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v4i1.2362

Abstract

Recent advancements in analyzing methods for the identification of arrhythmia based on deep learning have revealed great promise towards improving cardiac care. Probabilistic models have been used effectively to detect a number of arrhythmic disorders from ECG signals with the help of convolutional neural networks and Long Short Term Memory neural network. These models are more precise and quicker than conventional approaches to deal with the ailment in the initial stages and with diseases such as bradycardia, ventricular tachycardia, or atrial fibrillation. However, barriers such as class distribution, data sanitization, interpretability, and generalization across different types of patients remain, which hinders their clinical utilization. Actually, deep learning is used in clinical practice, especially in wearable devices and remote patient monitoring for the unceasing and real-time continuous rheological evaluation of the cardiovascular system. The subsequent advancements in this area will focus on the proper combination of the data from multiple subject areas and the application of specific treatment approaches, including the use of artificial intelligence in a more extensive medical system. Other than the diagnosis of arrhythmias, deep learning has the chances of enhancing patient prognoses, preliminary assessment, and tailor-made treatments. It is likely that deep learning-based systems will have a possibility to evolve into powerful aid for diagnosing and setting further treatment in cases of arrhythmias, though there are issues on the way to the enhance the availability and quality of the care. This will probably be facilitated by continued research and integration between academicians, practitioners, and policy makers.
Co-Authors Hafiz Khawar Hussain Hafiz Khawar Hussain Hira Zainab Hira Zainab Roman Khan