Applications of Artificial Intelligence in Cancer Diagnosis and Treatment

Clinical Snapshot

Key Findings

• P Value: Not reported at the review level

• Effect Size: Not reported — no pooled effect sizes are calculated; individual study metrics are discussed qualitatively

• Primary Outcome: Narrative synthesis of AI performance across cancer screening, diagnosis, theranostics, and personalised treatment — no single primary outcome is defined or quantitatively synthesised

• Nnt Or Sensitivity: Not calculable from this review — individual studies cited within the review may report sensitivity, specificity, and AUC values for specific AI diagnostic tools, but these are not synthesised or pooled

• Confidence Interval: Not reported at the review level

Clinical Application

Clinical feasibility of AI integration varies substantially by application. AI-assisted image analysis (radiology, digital pathology) is furthest along the translational pathway and has regulatory-approved tools in some jurisdictions. Multimodal data integration for treatment stratification and personalised therapy remains largely in research phases. Infrastructure requirements (data governance, interoperability, computational resources, clinician training) are significant barriers in most health systems. In Australia, the TGA regulates AI-based Software as a Medical Device (SaMD) under the medical devices framework, and several AI diagnostic tools have received TGA clearance for specific indications (e.g., AI-assisted mammography, retinal screening). The RACGP and Cancer Australia have not yet issued specific guidelines on AI integration into cancer care pathways. The Australian Digital Health Agency's national strategy and My Health Record infrastructure provide a potential data foundation for AI development, but data sovereignty, privacy (Privacy Act 1988), and equity of access across urban and rural settings remain critical considerations. PBS listing of AI-guided treatment decisions is not yet a feature of the Australian reimbursement landscape. Australian clinicians should treat this review as a conceptual overview rather than a practice-change document. Oncology patients across all tumour types in settings where AI-assisted diagnostic or treatment tools are being evaluated or implemented. Most directly applicable to radiologists, pathologists, and oncologists considering integration of AI decision-support tools into clinical workflows.

Abstract

Driven by changes in lifestyle and environmental factors, the global incidence of cancer is steadily increasing, which has established it as a leading cause of mortality worldwide. The current paradigm for cancer diagnosis and treatment relies on conventional methods, such as imaging, endoscopy, and tissue biopsy, which present significant limitations regarding sensitivity in early screening, diagnostic specificity, and personalized treatment. Consequently, the development of more efficient and accurate technologies remains a major objective in modern oncology research, and artificial intelligence (AI) has emerged as a particularly promising solution. Through machine learning and deep learning algorithms, AI is reshaping cancer care by enabling automated detection of minute lesions during screening and quantitative analysis of pathological features for diagnosis. It may also advance tumor theranostics through multimodal data integration for treatment stratification, response prediction, and image-guided or targeted therapeutic decision-making, whereas providing data-driven recommendations for personalized treatment. Despite these prospects, medical AI development faces several key issues, including data bias, model explainability, clinical reliability and generalizability, emerging limitations of foundation models and generative AI, and regulatory and ethical issues that need to be addressed. By reviewing recent advances in AI across screening, diagnosis, theranostics, and treatment, we aim to clarify where these methods are already useful, where evidence remains limited, and why closer collaboration among clinicians, engineers, and data scientists is needed for clinical translation. We hope this review serves as a practical reference for researchers and clinicians evaluating how AI may be integrated into oncology in a more standardized, clinically responsible way.

References

1. 1.Xie, Y., Wang, Z., Zeng, Z., Xin, T., Yuan, S., Qin, F., & Chen, Z. (2026). Applications of artificial intelligence in cancer diagnosis and treatment. Cancer Medicine. https://doi.org/10.1038/s41591-024-03434-4