To provide cutting-edge services based on Artificial Intelligence and high-performance computer (HPC) for the health domain is the main objective of BDS-ARI R&D specialists in this field.

Manuel Pérez

Health Head of Unit
Manuel Pérez

AI is opening a huge number of opportunities in healthcare, but further research and innovation must be done to unveil the complete potential of this technology, addressing specific requirements and barriers like compliance with existing regulations, ethical and trust concerns or the need to preserve data confidentiality. 
The main goal of the ARI’s Health team within Central R&D is to reinforce Atos portfolio with innovative functionalities tailored to the needs of healthcare stakeholders.


We firmly believe that AI and digitalization will revolutionize healthcare provision and drug discovery as they are already revolutionizing biomedical research. Together and complementary with AI, biomedical data interoperability, patient remote monitoring, Big Data and HPC will set up the bases for precision medicine focused on prevention, early diagnosis, accurate prognosis, and personalized treatments. The digital transformation of the healthcare system will be underpinned by:

  • Effective digital infrastructure for standardized and interoperable EHRs
  • Standard-based transformation of unstructured and uncodified medical data to standardised structures and medical terminologies-.
  • AI-based algorithms for unveiling biomedical complexity.
  • Innovative deep learning solutions to improve medical imaging efficiency.
  • AI-supported research for accelerating early-stage drug discovery.
  • AI-based solutions that speed up medical diagnosis and prognosis at the point-of-care.
  • AI-based solutions to predict patient outcomes and personalized treatments.
  • AI solutions to disrupt the current approach to clinical trials, from patient recruitment to adherence monitoring and data collection and analysis.
  • AI models for precision medicine by evaluating drug efficacy and discerning why it varies from patient to patient, enhancing the drug development process, and identifying the best drug for the right patient to improve treatment outcomes.
  • AI-built models in mHealth applications that democratize access to cutting-edge technologies reducing inequalities, facilitating healthcare in remote locations and giving healthcare professionals smart tools to make data-driven decisions in any situation.
  • Federated learning architectures that allow medical researchers to test AI models on distributed data in a privacy-preserving way.
  • Automatic integration and deployment for innovating faster through streamlining the software development and infrastructure management processes
  • Hybrid HPC-cloud-based infrastructures for supporting big data and computational demanding analysis.

The focus of the team is the development of innovative AI analysis applications for biomedical and clinical data analytics leveraging seamless and transparent exchanging of those data through the use of widely adopted research and industry healthcare standards. In particular:

  • Reinforced AI models for robust data-driven medical applications
  • Trustworthy AI models for medical acceptance
  • AI applications and uses cases for easing drug-discovery and clinical trials
  • DevOps tools and framework for automatic data-driven technologies implementation reducing AI development life cycle
  • Federated Learning Clinical data platforms following privacy by design principles

By using these AI-based services, organizations are ready to maximize the value of their raw health data with speed and accuracy.