Recent Advances and Ongoing Challenges in the Application of Machine Learning and Mathematical Models for Cancer Prognosis, Diagnosis, and Therapeutic Strategies

Abstract

Objective: Coronary artery disease (CAD) remains the leading cause 
of heart attacks and fatalities worldwide. Timely recognition of CAD 
requires swift measures for successful intervention. This research 
examines how machine learning (ML) and artificial intelligence (AI) 
methods contribute to the early detection and diagnosis of coronary 
artery disease.
Methods: The research conducted a comprehensive review of 
existing studies that utilized machine learning and artificial 
intelligence methods to detect colon cancer. In reviewing these 
research papers, the researchers accessed the databases of PubMed, 
IEEE Xplore, and Scopus.
Results: AI-driven models that integrate deep learning with machine 
learning algorithms offer efficient analysis of imaging data, clinical 
features, and biomarkers for the early identification of coronary 
artery disease (CAD). The innovative diagnostic methods have 
reduced examination durations and increased diagnostic precision 
compared to traditional techniques.
Conclusion: Employing AI and ML diagnostic methods that exceed 
human detection capabilities allows for the identification of 
numerous subtle patterns within extensive databases. Presently, AI 
and ML applications in medical settings face challenges related to 
programming flaws in discriminator algorithms and erroneous data, 
in addition to issues with acceptance in healthcare organizations. It is 
essential to further refine accuracy models and systematically 
integrate them into clinical practices.