Machine learning reveals why cancer trials fall short in real-world patients
- A new study shows that cancer trials often fail to represent real-world patients, leading to lower survival outcomes than reported in randomized controlled trials .
- The study found that high-risk patients experienced survival benefits that were 62% lower than RCT estimates, highlighting the gap in treatment effectiveness.
- Researchers developed an AI platform called TrialTranslator to help assess patient benefits from clinical trials, focusing on prognosis rather than strict eligibility criteria.
- The study suggests improving representation of high-risk groups in trials and emphasizes the potential of AI to enhance precision medicine in oncology.
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