Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC >0.8 in some retrospective studies

• Primary Outcome: AI model performance in predicting immunotherapy response

• Nnt Or Sensitivity: Performance deteriorates in external validation studies

• Confidence Interval: Not reported

Clinical Application

Currently limited by data heterogeneity, lack of prospective validation, and regulatory barriers Would require TGA approval for clinical decision support tools; aligns with Precision Medicine Initiative but implementation challenges remain significant Cancer patients considering immunotherapy, particularly those requiring personalized treatment selection

Abstract

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to create composite predictive signatures with significantly better discriminatory value. AUCs over 0.8 have been seen in a few retrospective studies with deep learning and ensemble models on whole-slide images, CT/MRI/PET radiomics, spatial and single-cell omics, and multi-omics fusion models, but prospective and multicentre validation is scarce, and external validation often shows deterioration in performance. AI is also used to enhance the translational pipelines of adoptive cell therapies (e.g., CAR-T) by improving patient selection, manufacturing (e.g., digital twins), and early toxicity prediction (e.g., CRS, ICANS). Nevertheless, clinical implementation remains hindered by data heterogeneity, bias, poor longitudinal validation, limited reproducibility, and a lack of transparency in most models, even though prospective, multicenter validation and explainable AI are crucial for clinician trust and regulatory acceptance. New systems such as federated learning, foundation models, spatial omics, digital twins, and wearable monitoring represent paths to generalizable, privacy-preserving, and actionable systems in clinical practice. To achieve the potential of AI, the generation of data will need to be standardized, reporting must be transparent, interdisciplinary, and regulatory frameworks must be strengthened focusing on the practical use of AI and patient safety. By taking these steps, AI could be shifted to prospective clinical decision support, which uses AI to meaningfully enhance personalization and outcomes in cancer immunotherapy based on a retrospective research tool.

References

1. 1.Nosa-Ihaza, E. A., Edeh, E. C., Geng, W. B., & Okenwa, E. (2026). Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment. Journal of the Egyptian National Cancer Institute. https://doi.org/10.1038/s41586-023-05881-4