Vol. 4 No. 2 (2024): Journal of Machine Learning in Pharmaceutical Research
Articles

Advancing Cancer Treatment with AI-Driven Personalized Medicine and Cloud-Based Data Integration

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  • Hassan Rehan
Hassan Rehan
Department of Computer & Information Technology, Purdue University, USA
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Published 23-08-2024

Keywords

  • artificial intelligence,
  • personalized medicine,
  • cloud computing,
  • cancer treatment,
  • machine learning,
  • deep learning,
  • genomic data,
  • data integration,
  • therapeutic targets,
  • clinical workflows
    • ...More
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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
H. Rehan, “Advancing Cancer Treatment with AI-Driven Personalized Medicine and Cloud-Based Data Integration”, Journal of Machine Learning in Pharmaceutical Research, vol. 4, no. 2, pp. 1–40, Aug. 2024, Accessed: Sep. 16, 2024. [Online]. Available: https://pharmapub.org/index.php/jmlpr/article/view/18

Abstract

The integration of artificial intelligence (AI) in the realm of personalized medicine represents a transformative advancement in cancer treatment, offering unprecedented opportunities for precision and efficacy in therapeutic strategies. This paper delves into the confluence of AI-driven methodologies and cloud-based data integration, elucidating their synergistic roles in advancing cancer care. Personalized medicine, a paradigm shift from traditional one-size-fits-all approaches, necessitates a deep understanding of individual patient profiles, including genetic, molecular, and phenotypic information. AI's application in this domain is revolutionizing the landscape by enabling more accurate and efficient analysis of complex datasets, thus facilitating the development of tailored treatment regimens.

AI-driven techniques, such as machine learning and deep learning, are instrumental in deciphering the intricacies of cancer genomics and identifying biomarkers that are crucial for personalized therapy. By leveraging large-scale genomic data and electronic health records, AI algorithms can uncover patterns and predict patient responses to various treatments, leading to more informed decision-making and optimized therapeutic outcomes. Moreover, these AI models are continuously refined through iterative learning processes, enhancing their predictive accuracy and reliability over time.

Cloud-based data integration plays a pivotal role in this advanced framework by providing a scalable and secure infrastructure for managing vast amounts of heterogeneous data. The cloud environment facilitates seamless data sharing and collaboration across research institutions and clinical settings, promoting the aggregation of diverse datasets that are essential for comprehensive cancer research. This data-centric approach not only enhances the accessibility of critical information but also supports the development of robust AI models by ensuring that they are trained on extensive and representative datasets.

The intersection of AI and cloud computing also addresses several challenges inherent in cancer treatment, including data fragmentation and interoperability issues. Cloud-based platforms enable the consolidation of disparate data sources, allowing for a more holistic view of patient information and facilitating the integration of multi-omics data. This integrated perspective is crucial for the identification of novel therapeutic targets and the customization of treatment protocols based on individual patient profiles.

Furthermore, the paper explores the implications of AI-driven personalized medicine on clinical workflows and patient outcomes. By automating routine tasks and enhancing diagnostic precision, AI technologies contribute to the reduction of human error and the acceleration of treatment processes. This not only improves the efficiency of clinical operations but also enhances the overall patient experience by providing more targeted and effective treatments.

Despite the significant advancements, several challenges remain in the implementation of AI-driven personalized medicine. Issues related to data privacy, algorithmic transparency, and the need for interdisciplinary collaboration are critical areas that require ongoing attention. The paper discusses these challenges and proposes potential solutions to mitigate them, ensuring that the integration of AI and cloud-based technologies continues to advance the field of oncology in a responsible and ethical manner.

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References

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