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0,$,#*UHQREOH $OSHV LV DQ LQWHUGLVFLSOLQDU\ LQVWLWXWH LQ $UWLÀFLDO ,QWHOOLJHQFH FRRUGLQDWHG E\ 3URI eULF *DXVVLHU ,W LV structured around two main themes: AI for the future and AI for humans and the environment. In addition to more targeted projects, four dedicated chairs and a joint research laboratory put AI at the service of health in line with the 4Ps (predictive, personalised, preventive, participative) medicine.
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utting AI at the service of “care” in an integrative health logic: this is the challenge of the “Deep Care: Patient Empowerment via a Participatory Health Project” chair. Headed by public health professor Philippe Cinquin, it applies Grenoble’s expertise in capturing real-life data to the capture of new data on health determinants (environment, social interactions, nutrition, etc.). The capture is carried out by micro and nanosensors that enable patients to be the actors of their own health. Four tasks have been defined. The first task aims to capture relevant data through various means: for example, electric bicycles and smart waistcoats to adapt a rehabilitation programme, sensors to characterise cardiac effort, swallowable micro-robots with biological programming to analyse the microbiota with a view to diagnosing a chronic disease with the start-up Pelican, or an implantable medical device to analyse physiological variations in order to prevent a relapse of heart failure with the start-up SentinHealth. The second task is to merge massive and heterogeneous data thanks to PREDIMED, a health data warehouse designed at the Grenoble Alpes University Hospi-
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tal, which makes it possible to identify health determinants that can improve patient care. The third task consists of correlating hospital data and human and social sciences (SHS) to propose a reorganisation of the emergency service of the Grenoble Alpes University Hospital, which has been weakened by the implementation of the T2A (fee-for-service). The fourth, more transverse task concerns the SHS: self-image and body anthropology are at the heart of tools co-constructed with patient associations. The Chair also collaborates with the MSA (agricultural social security - identification of new correlations between long-term illnesses and occupations), Thales (image processing for better management of undernutrition), ICAlps and ST Microelectronics (design of miniaturised intelligent implantable devices), several start-ups (Etisense, Texisense, eBikeLabs, Theia) and other industrial partners. A well-deserved recognition. Computer-assisted medical intelligence Co-directed by Jocelyne Troccaz, research director, and Sandrine Voros, research fellow, from the GMCAO team (Computer-Assisted Medical-Surgical Procedures TIMC-IMAG Laboratory - UMR CNRS
- UJF 5525), the “Computer-Assisted Medical Interventions (CAMI) Assistant” chair aims to develop a new generation of computerised intraoperative assistants based on AI, with the aim of treating patients more efficiently and less invasively. The fusion of big data, coupled with simulation and monitoring tools, could improve surgical strategy decisions and the quality of surgical procedures. Deep learning and machine learning are used to solve problems of medical image segmentation, to sort out relevant information from non-useable data or to better understand surgeons’ movements and to empower certain parts of the procedure where human added value is not essential. For example, in laparoscopic surgery, a questionnaire submitted to six surgeons recently showed that they tended to agree with the criteria chosen by the algorithms to make their decisions. Ultimately, these new techniques offer a new perspective on old problems. A potential to be exploited. Telecom4Health, a joint University of Grenoble Alpes - Orange research laboratory The result of a collaboration that began in 1999, this brand-new joint research Septembre 2021
