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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Cancer02:18

Cancer

Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.

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Related Experiment Video

Updated: Jun 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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

Connor D Yost1, Bradley Callas2, Peter Halligan2

  • 1Department of Internal Medicine, Creighton University School of Medicine, Phoenix, AZ 85013, USA.

Cancers
|June 26, 2026
PubMed
Summary

Current frameworks for evaluating artificial intelligence (AI) in oncology are insufficient. Mandatory adoption of existing evaluation frameworks is needed to ensure AI tool appropriateness for individual cancer patients.

Keywords:
ESMO ELCAPTRIPOD-LLMclinical decision supportevaluation frameworkexternal validationlarge language modelsmachine learningoncologyresponse prediction

Related Experiment Videos

Last Updated: Jun 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Oncology
  • Medical Artificial Intelligence
  • Clinical Evaluation Frameworks

Background:

  • Artificial intelligence (AI) and large language models (LLMs) are rapidly adopted in oncology for tasks like imaging, pathology, and treatment selection.
  • Physician use of AI in oncology is projected to double between 2023 and 2026.
  • Existing evaluation frameworks for AI tools have not kept pace with AI advancements, with many designed for specific lifecycle stages and not patient-specific appropriateness.

Purpose of the Study:

  • To review current AI evaluation frameworks in oncology, including general, oncology-specific, and LLM-specific guidance.
  • To identify the limitations of existing frameworks in addressing the unique challenges of AI in oncology, such as rapid standard of care changes and LLM-specific issues like hallucination.
  • To propose a mandatory adoption of existing frameworks and outline a cancer-aware evaluation pathway.

Main Methods:

  • Comprehensive review of existing AI evaluation frameworks, including TRIPOD+AI, PROBAST+AI, CLAIM, SPIRIT-AI, CONSORT-AI, DECIDE-AI, MINIMAR, CREMLS, ESMO EBAI, TRIPOD-LLM, MI-CLEAR-LLM, CHART, and ESMO ELCAP.
  • Analysis of framework suitability for the dynamic oncology landscape and specific challenges posed by LLMs.
  • Identification of gaps in current evaluation methodologies.

Main Results:

  • Numerous AI evaluation frameworks exist, but each addresses only a part of the evaluation problem and none are sufficient alone for oncology.
  • Existing predictive-model frameworks do not capture LLM failure modes like sensitivity to prompting, vendor updates, behavioral drift, and high hallucination rates.
  • Oncology's rapidly changing standard of care, assay drift, and small biomarker-defined subgroups necessitate robust and adaptable evaluation methods.

Conclusions:

  • The necessary frameworks for evaluating AI in oncology already exist; the critical missing element is their mandatory implementation.
  • Journals and regulators should transition from recommending to requiring the use of these frameworks.
  • A standardized, cancer-aware evaluation pathway with clear responsibilities for authors, reviewers, journals, and regulators is essential for safe and effective AI integration in oncology.