Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

Abstract

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species and electromagnetic emissions generated through gas plasma jets have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets or approaches within machine learning, such as reinforcement learning and deep learning. This review emphasizes the significant application of advanced algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of machine learning techniques in plasma medicine requires research efforts, data sharing, and interdisciplinary collaborations. Unraveling the complex mechanisms, developing real-time diagnostics, and optimizing advanced models will be crucial to harnessing the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records

“…In that direction, remarkable outcomes and results have been recently achieved through the implementation and utilization of advanced and sophisticated ML and AI algorithms and applications in various tasks within the healthcare domain. Noteworthy achievements in the tasks of personalized diagnostics [ 5 ], disease early risk identification [ 6 ], and personalized medicine [ 7 ] have been realized by employing ML models aiming to introduce enhanced and personalized prevention and intervention measures. However, the processing and analysis of vast numbers of datasets, ranging from medical images to secondary data collected from wearables and sensors, and from genetics to genomics, have revealed the need for the utilization of more complex algorithms when aiming to identify hidden patterns and integrate heterogeneous data in an optimum way.…”