2031: Artificial Intelligence conquers healthcare

2031. Catherine suffers from a chronic illness. From diagnosis to psychological support, from therapeutic guidance to taking medication, her care journey is shaped by digital interventions and artificial intelligence.

A journey to the future of patient care

2031. Catherine suffers from a chronic illness. From diagnosis to psychological support, from therapeutic guidance to taking medication, her care journey is shaped by digital interventions and artificial intelligence.

Made in cooperation with our partners from esanum.fr

 

This is a translated version of the original article written in French by Joris Galland, internal medicine specialist, who is currently based at the Bourg-en-Bresse Hospital (Paris, France). Dr Galland is passionate about new technologies, and is a frequent contributor on esanum.fr on technology, innovation, and trends, and how they impact the future of medicine and the medical profession. For this analysis, Dr Galland wishes to express collegial gratitude to Prof. Laurent Arnaud:
 

I would like to thank Prof. Laurent Arnaud, rheumatologist at the University Hospital of Strasbourg, for his participation (on Twitter: @Lupusreference). He is one of the co-authors of the study "Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?" which served as the basis for this article.1

Joris Galland
 

2021 has just come to an end, 2022 doesn't look too bright... We might as well move on to the year 2031, in which Delta and Omicron will hopefully only haunt those who swot up on ancient Greek at school.

So, it is 2031. In the last ten years, incredible progress has been made in medicine and computer science. Artificial intelligence (AI) is routinely used in hospitals, with the blessing of Generation Z (which has nothing to do with a short-lived political-media phenomenon of 2021/2022). These people who are now entering the labor market were born between 1997 and 2010 and have grown up in a digital environment. They are not afraid of algorithms, quite the opposite.

Attention: fiction!

Advances in genetics have also been rapid. It is now possible to have one's entire genome sequenced by taking a simple sample at a medical laboratory. Social security covers the cost of €40. Thanks to these advances, a new disease has been identified called "hyperalgesic tendon glycoproteinosis" (HTG).

Just ten years ago, many people with chronic muscle and joint pain were diagnosed as "fibromyalgics". Since then, powerful AI has enabled genetic analysis of a cohort of 302,000 patients and revealed a cluster of 236,000 individuals. A team of researchers discovered a mutation in the GPTO gene, which is responsible for the deficient glycoprotein GP235 and causes pain in adulthood.

Catherine is 35 years old and runs a communications company in the Bresse region. She is single and childless. For several months, she has been suffering from joint and muscle pain that interferes with her daily activities. Like many patients in 2031, she performs a pre-diagnosis online via the app Symptom Check, which records her symptoms and history.2,3

An algorithm estimates an 87% chance that she has HTG, far ahead of the hypothesis of rheumatoid arthritis or ankylosing spondylitis. Even before she sees a doctor who refines the diagnosis, the patient collects data via networked devices to assess the development of her symptoms ("symptom tracking").

Wearables, chatbots and co

In this case, the networked devices are mainly wearables, i.e. garments equipped with sensors that measure the decrease in muscle strength and joint circumference. A wristwatch indicates when the patient's heart is racing due to pain or anxiety. A pair of glasses records Catherine's sleep-wake phases during the day to measure her tiredness.4,5

  After collecting this data, Catherine searches for the best specialist for HTG on an online platform. Due to demographic trends in 2031, Catherine has to travel over 500 kilometres to Paris for a face-to-face meeting with an HTG specialist. Catherine opts for a video consultation and uploads the data she has collected as well as her lab results and imaging procedures to the platform.

Even before the video consultation, the platform's algorithm analyses and interprets the X-ray images.6 The physician looks at all the data stored in the electronic patient file, confirms the strong suspicion of HTG and orders biological and genetic tests.

Catherine orders a complete blood test online: she puts a few drops of blood on a blotting paper and sends it by post to the online analysis laboratory.7,8 The results are available the next day. And they are unequivocal: the genetic analysis confirms the presence of a genetic variant p.m643v in the GPTO gene. HTG is officially diagnosed.

This comes as a shock to Catherine, who is very active in sports and in her job. She registers on one of the many social networks that specialise in health topics and deal with this disease. As soon as she talks about her feelings and issues, an AI analyses her vocabulary and the frequency of her interactions to assess the intensity of her mood lows. Depending on the severity, the algorithm suggests she contact professionals or use relaxation apps.

On the platform connected to this network, Catherine also shares certain health data (data collected, medical documents, vital and bio parameters, X-rays, etc.). For the multinational company that owns this social network, this is a windfall - this way it accumulates Big Data provided free of charge and anonymously by thousands of patients with the same disease. This data is resold for research purposes9 and feeds AIs that specialise in diagnosing HTG. The circle is closing.

Catherine not only exchanges information with people in this network. Chatbots give her some hygiene and nutrition tips based on her profile and later encourage her to adopt a new lifestyle.10,11

Therapeutic education, drones and personalised follow-up

An AI assesses how well Catherine understands her disease. The functioning of her memory is analysed to determine the most favourable times of day to teach her new knowledge. This adaptive learning suggests an optimal learning path for her. Catherine is also informed in real time about the progress of HTG research and clinical trials in which she could participate. These trials now take into account around 40 parameters (in 2021 there were only a few).

With the advent of P4 medicine - tailored precision medicine - personalised treatment decisions are coming to the fore.12 To track Catherine's disease progression, apps detect the signs of early relapse via questionnaires or networked devices. The physician is notified in real time and adjusts treatment.13

In 2021, Catherine would have had to go to the pharmacy. It sounds like a time long ago. The rules for dispensing medicines have been significantly relaxed in Europe, and technological progress has done the rest. When ordering on Amazonia®, Catherine scans the QR code on her prescription. Her biotherapeutics are delivered to her garden by drone 45 minutes later. When she brings her smartphone near the package, the NFC chip in the smart packaging opens an app that speaks directly to her:

"Hello Catherine, it's chilly in the Bresse today! Since this is your first time using this type of treatment, would you like me to show you how to give a subcutaneous injection?"

In the case of oral treatment, another application reminds Catherine of the time of day.14 Compliance has improved considerably since the appearance of "connected capsules": detected by a patch or a smartphone, they deliver the drug content only at the scheduled time or downstream of the stomach, in order to optimise the distribution.15

In addition to medication, Catherine uses digital therapies. She follows a daily virtual reality programme for pain relief, which combines hypnosis and sophrology.16 A breathing amplifier helps her to calibrate her breathing to achieve a relaxing effect.17

Utopia or dystopia?

In this fiction - if it is not just a rough prediction - AI is gradually conquering healthcare. Is it an ally of the physician? Is it a competitor? Does it enable medical professionals to focus on non-delegable tasks, or does it run the risk of replacing them? Will it be possible to say in 2031 that it was AI that loosened the stranglehold on liberal medicine and the public hospital?

For this to happen, however, Catherine would have to accept that she will be cared for in this way from A to Z, without directly encountering a physician. "Impossible!", we are tempted to think. But we should remember that a virus and a few months were enough to establish remote medicine.

2031: The technology will be there, Generation Z will be waiting for us. Will we physicians be prepared for it?


References