Project description
AI tools for personalised cancer therapies
Machine learning can accelerate personalised medicine, demonstrating superior accuracy and speed compared to humans in diagnosis, outcome prediction and treatment recommendations. However, ensuring the trustworthiness of a model’s predictions throughout its life cycle is crucial. The EU-funded TAIPO project is dedicated to developing trustworthy AI tools for personalised oncology. This entails creating reliable algorithms for diagnosing and stratifying cancer patients, as well as establishing a framework for transparent modelling of personalised outcomes. The project enhances the reliability of AI models for three clinical applications: skin lesion classification, personalised outcome modelling for acute myeloid leukaemia, and therapy recommendations for metastatic breast cancer. TAIPO’s results are expected to advance trustworthy machine learning in the field of medicine.
Objective
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 models 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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
You need to log in or register to use this function
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
69120 Heidelberg
Germany