BEATCANCER: AI Powered Platform for Personalized Cancer Care and Treatment Planning

Abstract

This paper introduces an AI platform designed to improve personalized cancer care and treatment planning. The platform employs advanced machine learning algorithms to analyze patient information, including genetic data, medical history, and clinical results, to generate tailored treatment recommendations. By integrating diverse data sources, the system enables oncologists to take more accurate and information-based decisions on the treatment pathway while optimizing the process, minimizing risks, and side effects.It integrates well with the existing systems of healthcare, using predictive analytics to forecast treatment responses and adapt plans as the disease progresses, whereas the AI platform continually learns from new data, making better recommendations over time, and ensuring that ongoing treatment stays updated with current research as well as the individual patient\'s needs. The platform strives to revolutionize oncology by delivering personal, accurate, and data-informed treatment plans. It has the potential to improve clinical outcomes while reducing the burden on patients, thereby streamlining cancer care and, ultimately, enhancing the quality of the treatment process.

Introduction

Cancer remains a leading cause of death worldwide, with treatments often lacking personalized approaches that consider individual patient differences. Traditional methods can fall short since patients with the same diagnosis may respond differently. AI-powered platforms are emerging as powerful tools to personalize cancer care by analyzing large-scale clinical, genetic, imaging, and patient data to generate tailored treatment plans. These AI systems continuously learn and adapt, enhancing precision and effectiveness in oncology.

The literature review highlights various studies on AI applications in cancer care, including real-time patient monitoring via wearables, AI-based decision support systems, machine learning frameworks for personalized treatment, predictive analytics, blockchain for data security, and AI-driven treatment recommendation frameworks. Challenges noted include data privacy, integration, clinical validation, model transparency, and ethical concerns.

The proposed methodology involves an AI-based system that integrates comprehensive data sources—clinical, genomic, lifestyle, and patient history—and applies advanced machine learning to improve early detection, personalize treatments, and continuously monitor patients in real time. Patient feedback will be used to further refine treatment plans, optimizing outcomes and addressing side effects dynamically.

Conclusion

AI has been integrated into the medical field, especially within cancer care. This has provided a significant leap forward in medical technology. Early detection and personalized treatment planning is within the realm of what AI can do to transform cancer diagnosis and treatment. The reviewed literature has mentioned that there are many applications in AI these include some models of machine learning for particular treatment planning, deep learning for early detection, some predictive analytics for treatment results, and blockchain for data sharing in a secure environment. These advancements are promising but also marking challenges and gaps that lie ahead in the current state of AI in oncology. The proposed AI system is expected to transform cancer care by providing personalized treatment plans, improving early detection, and optimizing outcomes all while reducing costs and enhancing the quality of life for patients.

References

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Copyright

Copyright © 2025 Aryan Chaturvedi, Deepika Tyagi, Abhay Singh Patel, Abhay Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.