AI for Life Sciences

Our mission is to develop, validate, and disseminate new methods of Artificial Intelligence (AI) in the Life Sciences. Our interdisciplinary research combines methodological innovation in AI – from machine learning to deep learning and generative AI – with cutting-edge applications in digital medicine, aiming to advance the understanding of physiological and pathological processes and promote human health and well-being.

Main research and application areas:

• Generative AI for the creation of synthetic medical images
• Self-supervised learning techniques for biomedical imaging
• AI inspired by biological learning in neuronal cultures
• AI for healthy ageing: prediction of functional decline, falls, and adverse events
• AI for predictive, personalized, preventive, and participatory (P4) medicine:
  • AI for primary, secondary, and tertiary prevention
  • AI to predict lung cancer
  • AI for the design of digital risk screening tools and digital biomarkers discovery
• AI for augmented reality in endoscopy
• AI methods inspired by Neuroscience
• AI in movement analysis: physical activity and motor impairment classification
• AI and wearable technology to enhance patient care
• AI for the analysis and decoding of brain signals
• AI for infant cry analysis, human voice, speech, and language
• AI approaches to the genotype-phenotype relationship

ERC Fields 

• LS5_16 Systems and computational neuroscience (e.g. modelling, simulation, brain oscillations, connectomics)
• LS5_17 Imaging in neuroscience
• LS5_18 Innovative methods and tools for neuroscience
• LS7_1 Medical imaging for prevention, diagnosis and monitoring of diseases
• LS7_2 Medical technologies and tools (including genetic tools and biomarkers) for prevention, diagnosis, monitoring and treatment of diseases
• LS7_14 Digital medicine, e-medicine, medical applications of artificial intelligence
• PE7_9 Man-machine interfaces
• PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)

Scientific coordinator: Prof. Stefano Diciotti