Fernando de Obeso
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Being a Patient in 2050

By Fernando de Obeso | Mon, 10/18/2021 - 15:05

For this article, I wanted to write something different. An optimistic article on what I believe are the big improvements we will probably see in healthcare in the next 20-30 years. Despite what people may think, HUGE progress is being made to improve our overall health and life expectancy. Here are some reasons why.

  1. Fighting aging like a disease

Life expectancy has increased dramatically over the last 200 years in 1800, life expectancy was about 40 and now it is around 75). Unfortunately, lifespan has not grown proportionally in terms of being healthy, and the period of life when a person lives with disability and illness at the end of life is actually growing.

So, the major shift for the future is the notion that aging symptoms can be reversed, and now new evidence suggests that late-life ill health can be combated. In laboratory animals, genetic, lifestyle and pharmacological interventions can increase not only the lifespan but also the health span.

In humans, improvements in diet and the implementation of physical exercise regimes can result in major health improvements but a better lifestyle is not enough to prevent age-related diseases. The goal is that medical science will have progressed enough to enable people to have healthier and more active lives almost up until their eventual death.

Going forward, the direct targeting of aging mechanisms, including with existing drugs, presents an opportunity to reduce disability and illness in late life. Positive outcomes have been obtained with many existing drugs and new drugs, which target the underlying molecular mechanisms of aging, are coming that may help prevent age-related diseases, such as Parkinson’s and senile dementia.

  1. Technology and patient outcomes

There are many technology applications in healthcare but here are some concrete examples of how exactly this may develop in the future and what benefits a patient in 2050 will have.

Faster and better diagnosis

Diagnosing diseases takes many years of medical training. Even then, diagnostics is often an arduous, time-consuming process. Demand for experts exceeds the available supply and this has created bottlenecks in many hospitals that put physicians under strain and often delay life-saving diagnostics.

One major recent technological solution to this problem is the use of machine learning – particularly deep learning algorithms (DLA) that have made advances in automatically diagnosing diseases, making diagnostics faster and more precise.

How do machines learn to diagnose? Machine learning algorithms can learn to see patterns similarly to the way doctors see them. A key difference is that algorithms need a lot of examples – many thousands or sometimes millions – to learn. These examples need to be neatly stored and digitized. So, one solution is to take the raw data (without any software manipulation or interpretation) from the equipment and compare it to millions of data points from other patients in centralized databases, allowing the DLA to compare and detect problems that may escape the human eye. Machine learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized. Such as radiology imaging allowing for detecting lung cancer or strokes based on CT scans, assessing the risk of heart diseases based on electrocardiograms or cardiac MRI images, classifying skin lesions from skin images or finding indicators of diabetic retinopathy in eye images.

Since there is plenty of good raw data available in these cases, algorithms are becoming just as good at diagnostics as the experts. The difference is the algorithm can draw conclusions in a fraction of a second and it can be reproduced inexpensively all over the world. The advantages are obvious as one can see that soon everyone could have access to top experts in radiology diagnostics, and for a significantly lower price.

More advanced AI diagnostics are coming soon. The application of machine learning in diagnostics is just beginning – more ambitious systems involve the combination of multiple data sources (CT, MRI, genomics and proteomics, patient data and even handwritten files) in assessing a disease or its progression. Collaboration via this digitization of information will also allow a multi-doctor perspective on a patient’s disease and treatment, allowing for much better outcomes.

With ML, doctors will still have a role

It’s unlikely that technology will replace doctors, however. Instead, AI systems will be used to highlight potentially malignant lesions or dangerous cardiac patterns for the expert, allowing the doctor to focus on the interpretation of those signals. The cooperation between people and technology could have amazing results. Doctors will focus on reading between the lines of what machines will highlight.

The digital future

Technology could transform unsustainable healthcare systems into sustainable ones, equalize the relationship between medical professionals and patients, provide cheaper, faster and more effective solutions for diseases – technologies could win the battle for us against cancer or heart disease, as we are apparently winning versus COVID – and could lead to healthier individuals living in healthier communities. Technology can do for healthcare what line production did for the car over a century ago. However, given that human health is far more complex than building cars, we will also need to change our attitude toward the concept of medicine and healthcare.

The implementation of artificial intelligence in clinical practice is a promising area of development that is rapidly evolving, together with the other modern fields of precision medicine, genomics and teleconsultation. While scientific progress should remain rigorous and transparent in developing new solutions to improve modern healthcare, health policies should now be focused on tackling the ethical and financial issues associated with this cornerstone of the evolution of medicine.

  1. Precision medicine

The human genome contains 3 billion base pairs of DNA, uniquely arranged to give us our fundamental anatomy and individual characteristics. DNA forms genes and understanding their function provides crucial insights into how our bodies work and what happens when we get sick. This was the driving force behind the 13 years and billions of dollars spent on the Human Genome Project. Now, we can map a human genome in just a few hours and for less than a thousand dollars. Fast, large-scale, low-cost DNA sequencing has propelled genomics into mainstream medicine, driving a revolutionary shift toward precision medicine.

The Human Genome Project has made possible the discovery of nearly 2,500 disease genes and these are proving highly effective at providing fast and accurate analysis. They have been especially valuable for identifying rare genetic diseases that had previously taken years to diagnose, which has created a shift toward preventive medicine; in other words, using the human genome, we can prevent the onset of many genome-related diseases, such as cancer or diabetes. This prevention will allow us to not only combat the disease more efficiently but will also significantly reduce the related cost and side effects. Many of the new drugs approved by the FDA are targeted therapies. This is making researchers look at a disease on a personal level. This is evident in the latest technologies in gene editing, a technique that changes the DNA of living organisms. If a particular gene alteration is causing a problem, you cut it out, and replace it with good DNA created in the lab. Gene editing is becoming a fast, effective and increasingly affordable treatment. This is the ultimate tailor-made medicine.

More thoughts? Contact me at fdeobeso@saludfacil.org.

Photo by:   Fernando de Obeso