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Machine Learning Computational Advancements for peRsonalized mEdicine

Project description

AI to support smarter diagnoses

Doctors don’t always have all the information. Sometimes they need to make diagnosis decisions with incomplete information, even as hospitals collect massive amounts of patient data. From genetic tests to electronic health records, much of this valuable data remains siloed and underused. Supported by the Marie Skłodowska-Curie Actions programme, the MLCARE project aims to equip clinicians with AI tools that can bring it all together. By integrating genomic, clinical and environmental data, MLCARE will support more accurate diagnoses and personalised treatments. Crucially, the project focuses on building AI that is trustworthy, explainable and secure – essential qualities for real-world use. MLCARE will pave the way for smarter, more precise patient care.

Objective

Personalized medicine represents a paradigm shift in healthcare, transitioning from generalized treatment strategies to highly personalized care. This transformation is driven by rapid advancements in digital technologies, which generate massive volumes of data from diverse sources, including genomic sequences, high-resolution imaging, wearable devices, and electronic health records (EHRs). However, despite an annual estimate of 50 petabytes of data generated per hospital, only a fraction is utilized effectively. Harnessing this untapped potential offers a transformative opportunity to uncover novel disease mechanisms and deliver more precise and effective therapeutic interventions.

The MLCARE project seeks to revolutionize personalized medicine by developing cutting-edge AI solutions that integrate genomic, clinical, and environmental data into holistic, multimodal patient representations. Through innovative approaches such as Foundation Models and advanced generative AI methods, MLCARE will address critical challenges in processing complex, high-dimensional, and multimodal datasets. Emphasis will be placed on ensuring the trustworthiness, explainability, and security of AI applications to support real-world clinical decision-making.

A core mission of MLCARE is to train a new generation of interdisciplinary researchers with expertise spanning AI, computational biology, and healthcare. The program will equip doctoral candidates with the technical, analytical, and ethical skills needed to lead the future of patient-centered, AI-driven medicine. By combining state-of-the-art research with a comprehensive training program, MLCARE aims to set new standards in personalized healthcare, ensuring equitable, data-driven solutions that improve outcomes across diverse populations. This project is poised to position Europe at the forefront of global innovation in precision medicine, delivering a timely and impactful response to evolving healthcare challenges.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-DN-01

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Coordinator

UNIVERSIDAD CARLOS III DE MADRID
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 564 376,32
Address
CALLE MADRID 126
28903 Getafe (Madrid)
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Participants (10)

Partners (11)

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