A Concise Survey on Machine Learning, Deep Learning, and Quantum Machine Learning Architectures in Healthcare: Applications, Challenges, and Future Directions | ||||
| Egyptian Chinese Journal of Applied Sciences | ||||
| Volume 1, Issue 1, June 2025, Page 18-25 PDF (291.17 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ecjs.2025.396981.1001 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Basmala Tarek; Nouran Ali; Ramez Yousri; Soha Safwat Labib 
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| Faculty of Computers and Information System, Egyptian Chinese University, Cairo, Egypt | ||||
| Abstract | ||||
| Machine learning (ML) has become a cornerstone of modern artificial intelligence, enabling computers to recognize patterns, make decisions, and improve performance based on experience. This paper explores the fundamental categories of ML, including supervised learning, unsupervised learning, and deep learning, highlighting their methodologies, advantages, and challenges. Supervised learning relies on labeled data to train models for tasks such as classification and regression, whereas unsupervised learning identifies hidden patterns in data without predefined labels. Deep learning, a subset of ML, employs multi-layered artificial neural networks (ANNs) to extract complex features from raw data, leading to breakthroughs in f ields like computer vision and natural language processing (NLP). As the demand for more powerful learning models increases, quantum machine learning (QML) has emerged as a promising paradigm that integrates quantum computing with ML techniques. QML leverages quan tum properties such as superposition and entanglement to enhance computational efficiency, potentially surpassing classical models in optimization, clustering, and high-dimensional data analysis. Despite its transformative potential, QML faces challenges related to hardware limitations and algorithmic develop ment. This paper provides a comprehensive overview of ML, deep learning, and QML, discussing their real-world applications, limitations, and future directions in advancing artificial intelligence. | ||||
| Keywords | ||||
| Machine learning; natural language processing; Deep learning | ||||
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