SubsidieMeestersTrustworthy AI tools for personalized oncology
The project aims to develop trustworthy AI tools for personalized oncology to enhance diagnosis, outcome prediction, and treatment recommendations, ensuring reliability and transparency in clinical practice.
Projectdetails
Introduction
Modern machine learning algorithms have the potential to accelerate personalized medicine at 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.
Trust and Adoption
However, for widespread adoption in clinical practice, 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.
Project Goals
I will facilitate an efficient interaction of clinicians with AI models by developing trustworthy AI tools for personalized oncology:
- I will develop trustworthy AI tools and algorithms for diagnosis and stratification of cancer patients.
- I will establish a framework for reliable and transparent modeling of personalized outcomes and therapy decisions in oncology.
Expected Outcomes
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:
- Trustworthy AI-based skin lesion classification based on dermoscopic images.
- Stratification and personalized outcome modeling for patients with acute myeloid leukaemia (AML) based on omics data.
- Therapy recommendation for metastatic breast cancer patients based on electronic health records.
Impact
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 closing 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.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.999.225 |
| Totale projectbegroting | € 1.999.225 |
Tijdlijn
| Startdatum | 1-5-2023 |
| Einddatum | 30-4-2028 |
| Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERGpenvoerder
Land(en)
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