Immunotherapy has emerged as a promising and effective approach in cancer treatment by stimulating the body’s immune system to target and eliminate malignant cells. Despite its significant therapeutic potential, several challenges remain, including accurate patient selection, identification of appropriate therapeutic targets, and the minimization of adverse effects.
Artificial intelligence (AI) plays a critical role in addressing these challenges by analyzing complex genomic, proteomic, and clinical datasets. Machine learning and deep learning algorithms can accurately identify patients likely to respond to immunotherapy, enabling the development of personalized treatment plans while avoiding unnecessary interventions in low-response individuals.
A key application of AI is predicting the efficacy of immune checkpoint inhibitors such as PD-1 and CTLA-4. By integrating medical imaging and genomic data, AI models can forecast treatment outcomes, enhance diagnostic precision, and reduce healthcare costs. Furthermore, AI is increasingly used in drug development, where it simulates novel molecular structures and predicts their therapeutic efficacy, thereby accelerating drug discovery and lowering development expenses. AI also contributes to identifying and managing side effects, improving the safety profile of immunotherapy.
Nevertheless, the implementation of AI in oncology is not without limitations. These include the need for high-quality, annotated datasets, algorithmic interpretability, and ethical concerns such as data privacy, algorithm transparency, and psychological impacts of extensive genetic testing, excessive diagnostic testing, potential treatment discrimination, and unclear legal responsibilities.
This article concludes that with robust data infrastructure and the advancement of interpretable AI models, the full potential of AI in cancer immunotherapy can be realized. This synergy promises a major leap toward precision medicine and a brighter future in cancer care.