US EditionMachine 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.
6 Articles
6 Articles
New AI platform identifies which patients are likely to benefit most from a clinical trial
A new study demonstrates that a first-of-its-kind platform using artificial intelligence (AI) could help clinicians and patients assess whether and how much an individual patient may benefit from a particular therapy being tested in a clinical trial. This AI platform can help with making informed treatment decisions, understanding the expected benefits of novel therapies and planning future care.
AI platform identifies which patients are likely to benefit most from a cancer clinical trial
A study led by Winship Cancer Institute of Emory University and Abramson Cancer Center of the University of Pennsylvania researchers demonstrates that a first-of-its-kind platform using artificial intelligence (AI) could help clinicians and patients assess whether and how much an individual patient may benefit from a particular therapy being tested in a clinical trial.
New AI system aims to improve real-world application of clinical trial results
A new study led by Winship Cancer Institute of Emory University and Abramson Cancer Center of University of Pennsylvania researchers demonstrates that a first-of-its-kind platform using artificial intelligence (AI) could help clinicians and patients assess whether and how much an individual patient may benefit from a particular therapy being tested in a clinical trial.
New AI Solution "TrialTranslator" Helps Assess Clinical Trial Efficacy
A study published in Nature Medicine, led by researchers from Winship Cancer Institute of Emory University and Abramson Cancer Center of the University of Pennsylvania, examines the effectiveness of TrialTranslator, an AI-driven clinical trial solution. Developed by Ravi B. Parikh, MD, MPP and colleagues, this machine learning framework translates clinical trial results to real-world populations, aiding clinicians and patients in evaluating ther…
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BIOENGINEER.ORGRevolutionary AI Platform Pinpoints Patients Most Likely to Gain from
A groundbreaking study spearheaded by the Winship Cancer Institute of Emory University in conjunction with the Abramson Cancer Center of the University of Pennsylvania has unveiled an innovative artificial intelligence (AI) platform that stands poised to revolutionize the way clinicians and patients evaluate the potential benefits of therapies under investigation in clinical trials. This cutting-edge AI tool, dubbed TrialTranslator, provides a s…
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