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AI Attack and Defense for the Smart Healthcarecore

ANTIDOTE · Horizon Europe grant · 2025-01-01–2028-12-31

EC contribution

€1,600,800

Total cost

€0

Beneficiaries

9
About the data

Source: CORDIS (official EU open data), Horizon Europe. Framework HORIZON · call HORIZON-MSCA-2023-SE-01 · scheme HORIZON-TMA-MSCA-SE · topic HORIZON-MSCA-2023-SE-01-01. CORDIS record →

Objective

The smart healthcare domain utilizing a combination of Artificial Intelligence (AI) and medical Internet of Things devices is undoubtedly transforming the healthcare industry as it can deliver new applications and solutions that benefit patients, doctors and hospitals. Improved treatment, cost reduction and faster diagnosis are some of the advantages that smart healthcare brings to healthcare stakeholders. First, AI can be utilized to efficiently process data for improved disease diagnosis in medical images including liver lesion classification and segmentation, brain tumor segmentation, breast cancer detection, etc. Besides, AI can assist in securing the healthcare system from cybersecurity attacks that target the operation of medical IoT devices or sensitive medical data. However, the common assumption with AI is that the training, testing and deployment environment is benign and trustworthy. This assumption, however, does not hold true in general. As a matter of fact, research in this area has shown that small perturbations in the important features of AI models during training or testing phase can trivially undermine their performance. This gives rise to adversarial AI, in which attackers can trick healthcare AI models to degrade their diagnostic or cybersecurity detection performance. ANTIDOTE project objective is to create a sustainable European and inter-sectoral network of organizations working on a joint research programme in the interdisciplinary fields of Healthcare, AI and Cybersecurity. The participants will exchange skills and knowledge which will allow them to design and develop concrete mechanisms to evaluate the robustness of AI models and propose novel methods to ensure their secure, safe, resilient and robust operations in the healthcare domain. The outcomes of the ANTIDOTE project will have a significant benefit for European society, while strengthening the collaborative research between the different countries and sectors.

Beneficiaries (9)

OrganisationCountryRoleEC contributionSME
UNIVERSITY OF PIRAEUS RESEARCH CENTER EL coordinator €257,600
IONIAN UNIVERSITY EL participant €266,800
CONSORZIO NAZIONALE INTERUNIVERSITARIO PER LE TELECOMUNICAZIONI IT participant €184,000
UNIVERSITATEA NATIONALA DE STIINTASI TEHNOLOGIE POLITEHNICA BUCURESTI RO participant €174,800
INFOLYSIS P.C. EL participant €165,600 Yes
PDM E FC-PROJECTO DESENVOLVIMENTO MANUTENCAO FORMACAO E CONSULTADORIA SA PT participant €156,400 Yes
COFAC COOPERATIVA DE FORMACAO E ANIMACAO CULTURAL CRL PT participant €147,200
ISTRAZIVACKO RAZVOJNI CENTAR ZA BIOINZENJERING BIOIRC DOO RS participant €128,800 Yes
UNIVERZITET U KRAGUJEVCU RS participant €119,600

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