AI-Based Cancer Risk Assessment Models: Current Trends Overview

Abstract

Cancer risk assessment is crucial for early cancer detection and prevention, customized treatment plans, and and better results for patients. Artificial intelligence (AI) has transformed the healthcare sector in recent years by providing advanced techniques for improving cancer risk assessment. This review paper provides a thorough overview of the latest advancements in AI-based cancer risk assessment models. We examine a number of AI techniques, including deep learning and machine learning, and how they could be used to the analysis of imaging, genomics, and medical records, among other data sources. The article's primary focus is on significant advancements in AI models that improve workflow integration, interpretability, and predictive accuracy for healthcare settings. We also discuss the difficulties in implementing AI, such as data privacy issues, model biases, and the need for large annotated datasets. This study shows how artificial intelligence (AI) has the potential to transform cancer risk assessment and open the door to more accurate and customized healthcare methods by looking at recent advancements and case studies. To focus continued efforts in this quickly developing field, future research areas and approaches to overcoming present obstacles are also highlighted.

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