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Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18
Sai Kiran Kuchana1, Uday Kumar Repalle2, Nikhilesh V Alahari3
1Department of Internal Medicine, Kakatiya Medical College, Warangal 506007, India.
Cancers
|May 27, 2026
Summary
Artificial intelligence (AI) in oncology shows promise but has not yet translated algorithmic accuracy into genuine patient benefit. Further research and systemic changes are needed to ensure AI reduces cancer mortality equitably.
Area of Science:
- Oncology
- Artificial Intelligence
- Medical Informatics
Background:
- Artificial intelligence (AI) is rapidly advancing in oncology, impacting all stages of cancer care.
- Despite thousands of annual publications, a gap persists between AI's demonstrated performance and its real-world patient benefit.
- Most current AI evidence comes from retrospective studies, differing from clinical deployment conditions.
Purpose of the Study:
- To review the translational maturity of AI applications across 18 major malignancies.
- To assess AI's progress in fulfilling its potential in oncological care through an evidence-stratified, cross-cancer analysis.
- To differentiate between diagnostic accuracy and clinical utility in AI oncology research.
Main Methods:
- A structured narrative review of major databases (PubMed/MEDLINE, Embase, IEEE Xplore, Cochrane Library) and regulatory literature.
- Searches combined cancer-specific terms, AI methodologies, and translational outcome descriptors.
- A five-tier translational readiness framework was applied, distinguishing diagnostic accuracy from clinical utility.
Main Results:
- AI development varies significantly across cancer types, with breast and prostate cancers being most mature (Tier 1).
- Some AI tools have achieved regulatory approval but show performance disparities across demographic subgroups and lack randomized evidence for mortality reduction.
- AI in hematologic malignancies, sarcoma, and pediatric tumors faces significant structural and ethical barriers beyond algorithmic refinement.
Conclusions:
- Oncological AI has not yet achieved its clinical promise; diagnostic accuracy does not equate to patient benefit.
- Current AI systems risk amplifying health inequities due to performance disparities across demographics and settings.
- Systemic shifts in regulation, trial design, and infrastructure are crucial for AI to realize its potential in reducing global cancer mortality.
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