Project description
Assessing AI performance for a resilient healthcare system
Integrating AI and medical Internet of Things (IoT) devices is transforming healthcare by enhancing treatments, reducing costs, and enabling faster diagnoses. AI supports disease detection and helps secure healthcare systems against cyber threats. However, even small changes in data can compromise AI performance, making it vulnerable to adversarial attacks that jeopardise diagnostic accuracy. Supported by the Marie Skłodowska-Curie Actions programme, the ANTIDOTE project is establishing a sustainable European network of organisations focused on healthcare, AI, and cybersecurity research. Participants will collaborate to develop methods for assessing AI model robustness and ensuring secure, resilient operations in healthcare. The project’s outcomes aim to strengthen cross-sector and international collaboration.
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.
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. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencescomputer and information sciencesinternet
- medical and health sciencesclinical medicineoncology
- natural sciencescomputer and information sciencescomputer security
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-SE - HORIZON TMA MSCA Staff ExchangesCoordinator
185 33 PIRAEUS
Greece