Evaluation Frameworks for Predictive and Generative Oncology AI: Current Standards, Cancer-Specific Gaps, and a Path...

Artificial intelligence (AI) is now applied across oncology, including imaging, pathology, therapy selection, toxicity assessment, and survival modeling, and large language models (LLMs) have been adopted particularly quickly. Reported physician use of AI roughly doubled between 2023 and 2026. The frameworks used to evaluate these tools have not advanced at the same rate. Many are available, but each was designed for a single stage of the AI lifecycle, and none directly answers the question most relevant to a treating clinician: whether a given tool is appropriate for the individual patient under care. We reviewed the frameworks in current use, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, and CREMLS, together with the oncology-specific ESMO EBAI and the newer LLM-specific guidance (TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP). Each addresses part of the evaluation problem, but none is sufficient on its own for oncology, where the standard of care changes rapidly, assays drift, biomarker-defined subgroups are small, and a model validated in one period may perform poorly in the next. LLMs introduce additional challenges, including sensitivity to prompting, undisclosed vendor updates, behavioral drift, and hallucination rates approaching 50% on clinical quality-assurance tasks. The predictive-model frameworks were not designed to capture these failure modes. The central argument of this review is that the frameworks the field requires already exist; what is missing is their mandatory adoption. We propose that journals and regulators move from recommending these frameworks to requiring their use, and we outline a cancer-aware evaluation pathway together with the specific responsibilities of authors, reviewers, journals, and regulators in implementing it.