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TAIPO

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Trustworthy AI tools for personalized oncology

Modern machine learning algorithms have the potential to accelerate personalized medicine in a fast pace. To date, first tasks in medicine are being addressed with machine learning algorithms that surpass humans in terms of accura... ver más

30/04/2028

GERMAN CANCER RESE...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-05-01

ERC-2022-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

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GERMAN CANCER RESEARCH CENTE...

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Duración del proyecto: 59 meses Fecha Inicio: 2023-05-01
Fecha Fin: 2028-04-30

Líder del proyecto

DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Modern machine learning algorithms have the potential to accelerate personalized medicine in a fast pace. To date, first tasks in medicine are being addressed with machine learning algorithms that surpass humans in terms of accuracy and speed, including diagnosis, outcome prediction and treatment recommendation. However, for a widespread adoption in clinical practice, a good performance in terms of speed and accuracy is not sufficient: practitioners also need to be able to trust a model’s prediction in all stages of its life cycle.I will facilitate an efficient interaction of clinicians with AI models by developing trustworthy AI tools for personalized oncology: First, I will develop trustworthy AI tools and algorithms for diagnosis and stratification of cancer patients. Second, I will establish a framework for reliable and transparent modelling of personalized outcomes and therapy decisions in oncology.TAIPO will result in novel algorithms and software tools for quantifying and improving the trustworthiness of AI models that I will apply to three clinical applications: (i) trustworthy AI-based skin lesion classification based on dermoscopic images, (ii) stratification and personalized outcome modelling for patients with acute myeloid leukaemia (AML) based on omics data, and (iv) therapy recommendation for metastatic breast cancer patients based on electronic health records.TAIPO will increase the throughput of trustworthy diagnoses of skin lesions and pave the way for low-cost access to diagnostic care. It will empower clinicians to make personalized and reliable therapy decisions, which we will demonstrate at the example of AML and metastatic breast cancer. Our novel algorithms to evaluate and improve the reliability of AI models are a crucial contribution to close the gap between in-silico AI-bench and bedside and will further push the field of trustworthy machine learning with many applications of AI in medicine.

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