INTEGRATING NATURAL PRODUCT-BASED THERAPEUTICS WITH AI-DRIVEN LUNG CANCER PREDICTION MODELS

Abstract

Here is the revised abstract without the citations:

Lung cancer is one of the leading causes of cancer-related deaths globally, with early detection and accurate prediction playing a critical role in improving patient outcomes. The healthcare industry has increasingly turned to machine learning (ML) and artificial intelligence (AI) to help identify at-risk individuals by analyzing vast amounts of data, including clinical, lifestyle, and environmental factors. While AI models have shown promise in predicting lung cancer risk, their effectiveness can be limited by the breadth of data they consider. To enhance these models and improve their predictive accuracy, integrating natural product-based therapeutics offers a promising solution. Natural products, derived from plants, fungi, and microorganisms, have long been recognized for their medicinal properties. Many of these compounds have demonstrated anti-cancer effects, including anti-inflammatory, antioxidant, and apoptosis-inducing properties. Despite their known benefits, the role of natural products in lung cancer prevention and treatment remains underexplored in AI-driven predictive models. This white paper presents a framework to integrate natural product data into AI-based lung cancer prediction models, offering an innovative, interdisciplinary approach to cancer risk management and prevention. By incorporating natural product data into machine learning algorithms, we can create a more comprehensive and personalized approach to predicting lung cancer risk. This integration will allow healthcare providers to not only assess genetic, environmental, and lifestyle factors but also consider the therapeutic potential of natural products in reducing cancer risk. Moreover, these enhanced AI models could offer personalized prevention strategies, such as recommending specific natural products or lifestyle modifications based on an individual’s unique risk profile. This white paper outlines the benefits of this integration, including improved predictive accuracy, the ability to offer personalized recommendations, and the potential to intervene early in at-risk populations. We also explore how incorporating natural product data could lead to more cost-effective healthcare by preventing or slowing the progression of lung cancer through early intervention. Ultimately, this integrated approach could help transform the way healthcare providers predict and manage lung cancer risk, leading to better patient outcomes and a reduction in the global cancer burden.

The synergy between AI-driven predictive models and natural product-based therapeutics presents an exciting opportunity to innovate lung cancer prevention strategies, paving the way for more individualized, precise, and effective approaches in managing cancer risk. By leveraging AI to enhance our understanding of how natural products interact with cancer biology, we can not only improve prediction accuracy but also offer proactive, personalized care that could save lives and reduce long-term healthcare costs.