A REVIEW ON ROLE OF GENERATIVE AI IN CANCER IMMUNOTHERAPY

Abstract

Cancer immunotherapy has transformed the way we approach cancer treatment. By harnessing the body’s immune system to target cancer cells, it has created new opportunities for both patients and doctors. However, the reality is still challenging—results from these treatments vary widely between patients, and cancer can often adapt to resist therapy. Background: Creating new immunotherapies is no easy task. The journey is complex and filled with obstacles. But now, generative AI is beginning to make a difference. Thanks to its rapid development, it provides a way to accelerate the search for new therapies. Objective: This project set out to explore just how much generative AI can contribute to cancer immunotherapy. The primary objectives: identify new therapeutic targets, predict patient responses to treatment, and refine immunotherapy strategies. Methodology: To achieve this, I thoroughly examined research on both generative AI and cancer immunotherapy. I analyzed various generative AI models—like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and others—to understand how they might apply to this area. The project also involved the development of a novel generative AI model for predicting treatment outcomes in cancer patients. Results: The project demonstrated the potential of generative AI in identifying novel therapeutic targets and predicting treatment outcomes in cancer immunotherapy. The developed AI model showed promising results in predicting patient responses to immunotherapy. Conclusion: Ultimately, this project shows that generative AI holds real promise for accelerating progress in cancer immunotherapy, improving treatments, and reaching more patients. There is still much to learn before these approaches become routine in clinical settings, but now is the moment for increased research and a concerted effort to integrate these tools into everyday cancer treatment.

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