May 27, 2022
A rising population of elderly patients. A shortage of doctors. Limits on in-person visits with health care providers.
These trends are driving healthcare to a fourth industrial revolution, what IEEE member Eros Pasero, a professor at Politecnico of Turin, calls “Medicine 4.0.” Previous revolutions in medicine, in his view, include discovery of DNA’s double helix structure, the sequencing of the human genome to help understand the origins of disease and the convergence of biology and engineering to develop medical devices.
The ideas behind this new paradigm in health sciences are the subject of Pasero’s lecture “Medicine 4.0: When New Technologies Work with AI,” which he offered as part of the Fall 2021 IEEE Instrumentation and Measurement Society Virtual Distinguished Lecturer Series.
It’s a discussion that weaves together the newest frontiers in telemedicine, artificial intelligence, medical devices and connectivity to present a new vision of healthcare.
What are the demographic drivers of telemedicine?
After World War II, increasing economic well-being led to a baby boom, which today has resulted in a huge increase in the number of elderly people. Doctors now treat more complex diseases in a population with longer life expectancy, and the number of doctors hasn’t risen. Telemedicine is the only way to allow doctors to manage these large numbers of patients.
How do those demographic drivers impact the design of medical devices?
Limited financial resources limit the number of doctors available to care for the elderly. Remote medical devices can help monitor multiple patients remotely without increasing the number of doctors. Additionally, the use of these simple medical devices can overcome limits on patient visits through COVID.
Your lecture touched on the idea of Medicine 4.0. What is it? And how does it differ from Medicine 3.0?
One of the possible applications of industry 4.0 is a new healthcare model. Medicine 3.0 represented the moment of the transformation of the way of operating through the introduction of technology in the work process. Medicine 4.0, through digitalization, can create virtual copies of the patient and use machine learning to reduce the possibility of error to zero, and also reduce costs and improve the quality of the service.
What are some of the advantages of integrating neural networks and artificial intelligence into remote medical devices and wearables?
Blood pressure is a great example. The gold standard in blood pressure measurement is extremely invasive, and is usually only done on patients that are in the ICU. It measures arterial pressure from inside the artery. Most of us are familiar with the blood pressure cuff, which is pretty good, but not quite as precise as the gold standard and requires a health care professional to operate. And then there is the automatic cuff, which has some other shortcomings: it’s not so precise, and it’s subject to users simply putting their arm in the wrong way.
But, with an AI database, we can build a model of blood pressure using other variables associated with the beating of the heart, like pulse wave velocity and pulse transit time. When these factors are measured with a wearable device similar to a fitness tracker, our model was slightly more precise than a blood pressure cuff. And, the patient could take the measurement on their own.
Remote medical devices are important for monitoring remote patients. Artificial intelligence and neural networks make it possible to make predictions about the patient’s status in the future. AI can also be used to understand a patient’s particular vital signs.
What are some of the challenges of using AI and neural networks in remote medical devices?
One challenge can certainly be to replace certain functions of the doctor using AI. Remote diagnosis of some simple diseases can be done using AI, giving doctors more time to check for complex diseases.
What’s the research agenda for technologists working in this space? What big problems need to be solved next?
Digital health needs AI. Today 19% of healthcare leaders are prioritizing investments in AI but 37% plan to do so over the next three years. But there are several obstacles to the digital transformation of the sector. There is a need to overcome the lack of technology experience among staff, the challenges of governance, interoperability and data security.
From a scientific point of view, the tools of AI should be evaluated: reliability, precision, accuracy and other parameters must be validated by regulatory agencies.
You can learn more about the ideas behind Medicine 4.0 by watching Prof. Pasero’s full lecture through the IEEE Instrumentation and Measurement Society.
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