Polytechnique insights A REVIEW BY INSTITUT POLYTECHNIQUE DE PARIS
Polytechnique insights A REVIEW BY INSTITUT POLYTECHNIQUE DE PARIS
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π Health and biotech π Science and technology

Digital innovations for better health

6 episodes
  • 1
    Alzheimer's, Parkinson's: “tommorrow, AI will detect disease”
  • 2
    Digital avatars of patients lungs
  • 3
    How to provide access to patients health data
  • 4
    Why the medical AI revolution could never happen
  • 5
    How digital technology will personalise healthcare
  • 6
    How we can better predict future epidemics
2_aiMaladies
Épisode 1/6

Alzheimer's, Parkinson's: “tommorrow, AI will detect disease”
Agnès Vernet, Science journalist
On April 27th, 2022
5 min reading time
Mounim El Yacoubi
Mounîm A. El Yacoubi
Professor at Télécom SudParis (IP Paris)

Key takeaways

  • AI can help go beyond current tests to provide the medical community with solutions that  are less expensive, less invasive and help refine diagnoses.
  • For Parkinson's disease, a European research project is being conducted by researchers  in collaboration with the Institut du Cerveau et de la Moelle épinière.
  • The aim is to be able to detect abnormalities typical of the disease using only a patient's  voice and facial expressions.
  • For Alzheimer's disease, the data could make it possible to follow up on how writing,  walking and voice changes over several months, things that are difficult for a doctor to follow objectively.
  • Health data could one day therefore be collected via watches, fridges, computers in order  to monitor the evolution of risky behaviour and habits.
Radiology Doctor working diagnose treatment virtual Human Lungs
Épisode 2/6

Digital avatars of patients lungs
Agnès Vernet, Science journalist
On April 27th, 2022
3 min reading time
Cécile Patte
Cécile Patte
Inria engineer in biomechanics, Jeunes Talents France 2020 prize "For women in science" (L'Oréal-Unesco)

Key takeaways

  • To improve treatments, engineers are seeking ways to adapt medical interventions to suit the specific biomechanics of each patient.
  • In order to avoid invasive testing, the MΞDISIM team develops ways to generate digital models of patients’ organs.
  • Cécile Patte is working on a tool to create digital avatars of the lungs of patients suffering from pulmonary fibrosis – a chronic lung disease and one of the long-term effects of Covid-19.
  • These digital replicas will enable doctors to evaluate personalised treatments non-invasively.
1_centralisationDonnees
Épisode 3/6

How to provide access to patients health data
On April 27th, 2022
3 min reading time
Emmanuel Didier (2)
Emmanuel Didier
Sociologist, CNRS Research Director and member of the Maurice Halbwachs Centre at EHESS

Key takeaways

  • The Health Data Hub is a French project with a budget of almost €10 million which aims to centralise all health data in France.
  • This project will make doctors; work easier thanks to the pooling of health data, but it will also open up a new market for companies.
  • The amount of health data in France is colossal: the Sniiram (the national inter-regime information system of the French health insurance system) holds 1.2 billion medical records collected since 2002.
  • However, centralisation poses a problem for some parties who do not want to entrust their databases at the risk of losing the work invested in them. This is the case for the Constance cohort which has a large quantity of data.
  • Other criticisms revolve around the project, notably the very principle of centralisation or the question of hosting, which would be done by Microsoft.
Épisode 4/6

Why the medical AI revolution could never happen
Joël Perez Torrents, PhD student at I³-CRG* at École Polytechnique (IP Paris)
On November 16th, 2022
5 min reading time
PEREZ TORRENTS Joël
Joël Perez Torrents
PhD student at I³-CRG* at École Polytechnique (IP Paris)

Key takeaways

  • Artificial intelligence (AI) for medical applications has the potential to profoundly change healthcare practices in the long-term: diagnostics, treatment, and patient experience.
  • But, until now, most developments in AI have followed a continuity of medical efforts rather than completely overturning pre-existing methods.
  • Deployment of medical AI is reduced at both at the institutional and individual level by the conservative environment around healthcare, where innovation is slow-moving.
  • The technical nature of AI reduces the disruptiveness of new applications, as it uses already existing data.
Telehealth, digital tablet and doctor hands for hospital innovation, software management and results update in dark workplace. Healthcare, cardiology and telecom medical professional with technology
Épisode 5/6

How digital technology will personalise healthcare
On November 7th, 2023
3 min reading time
Etienne Minvielle
Etienne Minvielle
CNRS Research Director and Professor of Health Management at Ecole Polytechnique (IP Paris)
Alexis Hernot
Alexis Hernot
Co-founder and CEO of Calmedica

Key takeaways

  • Digital technology has brought about a surge in innovation in the healthcare sector.
  • It is expected to improve the quality of patient care through remote monitoring.
  • Automated, digitised monitoring helps to prevent the toxic effects of a drug, personalise the care pathway for each patient and reduce the time spent in hospital.
  • In the long term, these advantages will help to meet the major challenges facing hospital structures, by relieving congestion in emergency departments and alleviating the shortage of medical staff.
  • These systems would free up 90% of a nurse’s time, improving the quality and quantity of patient care.
Male doctor showing realistic graph data of health care and health insurance business and growth with medical and medical financial analysis.Generative AI
Épisode 6/6

How we can better predict future epidemics
On January 10th, 2024
4 min reading time
Etienne Minvielle
Etienne Minvielle
CNRS Research Director and Professor of Health Management at Ecole Polytechnique (IP Paris)
Antoine Flahault
Antoine Flahault
PhD in biomathematics

Key takeaways

  • Since the 20th Century, various types of model have been used to predict the risk of epidemics, and have proved their effectiveness.
  • Big Data has enabled these models to evolve, now used to anticipate epidemics more effectively, so that humanitarian aid can be concentrated in the area at risk, at the key moment.
  • However, a number of challenges remain. Adapting predictions to local contexts, and the transition from prediction to action, which is hampered by socio-economic factors.
  • Combining Big Data processing, epidemiological expertise and algorithmic processing would multiply the potential of these models.

Contributors

Agnès Vernet

Agnès Vernet

Science journalist

After her initial studies in molecular biology, Agnès Vernet trained as a science journalist at ESJ-Lille. For the past 14 years, she has been writing for various media, scientific magazines, professional titles and general press, in France and Switzerland. Since 1st February 2021, she is the elected President of the French association of science journalists (AJSPI).

Other contributions

One Health: efforts to find new antibiotics go beyond science
Key data: the antimicrobial resistance crisis 
The post-antibiotic era: a health disaster is predicted
Digital avatars of patients lungs
Alzheimer's, Parkinson's: “tommorrow, AI will detect disease”
Voir toutes ses contributions
PEREZ TORRENTS Joël

Joël Perez Torrents

PhD student at I³-CRG* at École Polytechnique (IP Paris)

Joël Perez Torrents is a PhD student at *Centre de Recherche en Gestion (I3-CNRS) at Institut Polytechnique de Paris. His research focuses on the use of Artificial Intelligence in the healthcare system. He takes advantage of the technical courses of the Polytechnic engineering cycle (X16) and of the management research of the PIC master to make an "ethology" of digital instruments.

Other contributions

Why the medical AI revolution could never happen

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