AI for Life Sciences

Our mission is focused on the development, evaluation and dissemination of new Artificial Intelligence (AI) methods in Life Sciences. Our interdisciplinary AI research includes methodological advances in the foundations of AI along with the development of digital medicine applications to improve the understanding of healthy and pathological processes and advance patient health. Patient-specific AI models are designed by exploring both conventional machine learning and modern deep learning strategies. 

The main fields of application are: 

• AI for early detection of autism spectrum disorders
• AI in Neurology: prediction of cognitive decline and dementia 
• 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 robot-assisted surgery 
• AI methods inspired by Neuroscience 
• AI in movement analysis: physical activity and motor impairment classification 
• AI for the analysis and decoding of brain signals
• AI for infant cry analysis, human voice, speech, and language

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