Author(s) :

Ashwin Naik.

Volume/Issue :

Abstract :

Cancer immunotherapy has transformed oncology by enabling the immune system to target tumors, yet significant variability in patient responses underscores the urgent need for personalized treatment approaches. Artificial Intelligence (AI), through advanced machine learning and deep learning techniques, offers powerful tools to analyze complex, multi-dimensional patient data—including genomics, proteomics, imaging, and clinical records—to predict individual responses, optimize therapy selection, and monitor treatment outcomes dynamically. This article explores the development of AI algorithms tailored for personalized cancer immunotherapy planning, detailing the data integration processes, modeling strategies, and personalization frameworks that enable precise and adaptive treatment regimens. It also addresses challenges such as data heterogeneity, model interpretability, ethical considerations, and regulatory hurdles. By reviewing current clinical applications and envisioning future innovations, the article highlights AI’s transformative potential to enhance diagnostic accuracy, improve patient outcomes, and reduce adverse effects in cancer immunotherapy. Multidisciplinary collaboration and patient-centered design are essential to realize AI-driven precision oncology, ultimately advancing more effective and safer cancer care worldwide.

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