Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review, not a systematic review with defined PICO |
| I — Intervention | Not applicable |
| C — Comparator | Not applicable |
| O — Outcomes | Not applicable |
Bottom Line
This narrative review provides a broad overview of AI applications in oncology, focusing on multimodal approaches integrating imaging, pathology, and genomics. While the topic is highly relevant and the authorship credible, the narrative format limits the strength of evidence synthesis. The review lacks systematic methodology, making it unsuitable for evidence-based practice recommendations. Clinicians should view this as educational background rather than definitive guidance. The discussion of ethical and legal implications is valuable, but specific implementation recommendations require more rigorous systematic evidence. Australian clinicians should additionally consider TGA regulatory requirements and local implementation frameworks when evaluating AI technologies in oncology practice.
Key Findings
Effect Size: Not reported
Primary Outcome: Not applicable - narrative review format
Nnt Or Sensitivity: Not applicable - no quantitative synthesis provided
Clinical Application
Implementation challenges noted but specific feasibility data not systematically evaluated No specific Australian context discussed. TGA approval pathways for AI medical devices and integration with existing oncology services would need separate consideration Oncology patients and healthcare providers implementing AI technologies
Abstract
Artificial intelligence (AI) is reshaping cancer research and clinical oncology by enabling large-scale analysis of complex biomedical data. Although early AI efforts focused on single-modality tasks such as imaging interpretation or pathology classification, contemporary oncology increasingly requires integrated reasoning across heterogeneous inputs, including imaging, digital pathology, and genomics. Multimodal artificial intelligence (MMAI) models represent a major evolution by fusing these data types into unified predictive systems capable of informing diagnosis, risk stratification, treatment selection, and disease monitoring. This manuscript reviews the state of AI in oncology and highlights emerging foundation models within the field. There will be a focus on histopathology, and how this can be incorporated into risk assessment models. There will also be examples of MMAI utility in radiology and spatial proteomics. Additionally, we will discuss ethical and legal implications of MMAI in modern oncology, and recommendations for responsible application within clinical oncology practice.
References
- 1.Parkinson, G., Patel, R., Bergstrom, C., Vadasz, B., Amgad, M., Cooper, L. A. D., Sparano, J., & Lu, J. (2026). Artificial Intelligence in Oncology: Clinical Applications, Challenges, and Opportunities. American Society of Clinical Oncology Educational Book, 46. https://doi.org/10.1200/EDBK-26-520716
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