What Are the Applications of AI in Immunotherapy Selection?

Author: Rasit Dinc

Introduction

Immunotherapy has revolutionized cancer treatment by harnessing the body's own immune system to fight malignancies. However, the success of immunotherapy is not universal, with patient responses varying significantly across different cancer types and individuals. This variability underscores the critical need for precise patient selection to maximize therapeutic benefits while minimizing exposure to ineffective treatments and potential side effects. Artificial intelligence (AI) is emerging as a powerful tool to address this challenge, offering sophisticated methods to analyze complex biological data and predict treatment outcomes. By integrating AI into clinical workflows, oncologists can make more informed decisions about which patients are most likely to benefit from immunotherapy, paving the way for a new era of personalized cancer care. [1]

AI-Powered Predictive Models for Patient Stratification

One of the most significant applications of AI in immunotherapy is the development of predictive models that can stratify patients based on their likely response to treatment. These models leverage machine learning algorithms to analyze vast datasets, including clinical information, genomic data, and imaging features, to identify complex patterns that are not apparent to human observers. For instance, AI models can integrate data from a patient's electronic health record, such as age, sex, and comorbidities, with tumor-specific information, like mutational load and gene expression profiles, to generate a predictive score. This score can then be used to classify patients into different risk groups, allowing clinicians to prioritize immunotherapy for those who are most likely to benefit. A notable example is the SCORPIO tool, which uses routine blood tests and medical records to predict patient survival and tumor response to immune checkpoint inhibitors. [2]

Machine Learning Algorithms for Biomarker Discovery

In addition to predicting treatment response, AI is also instrumental in discovering novel biomarkers that can guide immunotherapy selection. Traditional biomarker discovery is often a lengthy and resource-intensive process, but machine learning algorithms can accelerate this by identifying subtle patterns in high-dimensional data that are associated with treatment outcomes. For example, AI can analyze the tumor microenvironment, including the spatial distribution of immune cells and the expression of immune-related genes, to identify new biomarkers that are more predictive than existing ones, such as PD-L1 expression and tumor mutational burden (TMB). By uncovering these complex relationships, AI can help to identify new therapeutic targets and develop more effective combination therapies. [3]

Deep Learning for Image Analysis in Immunotherapy

Deep learning, a subset of AI, is making significant strides in the analysis of medical images, such as pathology slides and radiological scans. In the context of immunotherapy, deep learning models can be trained to identify features in these images that are predictive of treatment response. For example, a deep learning model can analyze a digital pathology image to quantify the infiltration of immune cells into the tumor, a key indicator of an active anti-tumor immune response. Similarly, these models can analyze radiographic images to assess changes in tumor size and morphology over time, providing a more objective and reproducible measure of treatment efficacy. By automating the analysis of these images, deep learning can help to standardize the assessment of immunotherapy response and provide a more accurate prediction of patient outcomes. [4]

Challenges and Future Directions

Despite the promise of AI in immunotherapy, several challenges must be addressed before these technologies can be widely implemented in clinical practice. One of the main challenges is the need for large, high-quality datasets to train and validate AI models. Data sharing and standardization are critical to overcoming this hurdle. Another challenge is the "black box" nature of some AI models, which can make it difficult to understand the reasoning behind their predictions. Explainable AI (XAI) methods are being developed to address this issue and increase the transparency and trustworthiness of AI models. [5]

Conclusion

In conclusion, AI holds immense promise for revolutionizing the selection of patients for immunotherapy. By leveraging the power of machine learning and deep learning, AI can help to identify the patients who are most likely to respond to treatment, discover novel biomarkers, and provide a more objective assessment of treatment response. While challenges remain, the continued development of AI technologies, coupled with a commitment to data sharing and collaboration, will undoubtedly pave the way for a new era of personalized and effective cancer immunotherapy.

References

[1] Olawade, D. B., et al. (2025). Integrating AI into Cancer Immunotherapy-A Narrative .... PubMed. https://pubmed.ncbi.nlm.nih.gov/39851488/

[2] Reynolds, S. (2025, February 26). AI Tool May Help Guide Use of Cancer Immunotherapy - NCI. National Cancer Institute. https://www.cancer.gov/news-events/cancer-currents-blog/2025/ai-predicts-cancer-immunotherapy-response-survival

[3] Bhushan, R., et al. (2025). The role of artificial intelligence in predicting cancer .... ScienceDirect. https://www.sciencedirect.com/science/article/pii/S277290602500055X

[4] Rakaee, M., et al. (2025). Deep Learning Model for Predicting Immunotherapy .... JAMA Oncology. https://jamanetwork.com/journals/jamaoncology/fullarticle/2828578

[5] Saad, M. B., et al. (2025). Machine-learning driven strategies for adapting .... Nature Communications. https://www.nature.com/articles/s41467-025-61823-w