This month marked the crystal anniversary of the life sciences networking event Knowledge for Growth. The event was a huge success, with 1300+ attendees representing 600+ companies from 20+ countries around the world. The theme for this year was “Precision in Life Sciences”, with the fascinating plenary talks discussing the effect of digitalization on healthcare.
By Amy LeBlanc
A timely transformation
In recent years, few industries have seen such dramatic changes as the health sciences. The digitalization of healthcare is well underway, with disruptive technologies such as artificial intelligence (AI), big data and telemedicine taking the field by storm. Digital health is now a multi-disciplinary domain involving a range of stakeholders, from researchers, clinicians and doctors to the patients themselves.
This digital transformation and move towards precision medicine couldn’t have come at a better time. As improvements in medicine and nutrition have given rise to higher life expectancies, older populations are experiencing increasing levels of degenerative and lifestyle-related health issues. According to the World Health Organization, these types of non-communicable diseases currently account for 75% of deaths worldwide, representing tens of millions of people suffering from reduced quality of life. The economic impact is astronomical, with the World Economic Forum predicting that just five of these non-communicable diseases may cost the global economy $47 trillion as early as 2030.
Read this previous BioVox article for more on personalized healthcare.
At Knowledge for Growth, we were fortunate enough to hear from three experts in the field of digitalized healthcare: Jonathan Berte (CEO of Robovision), Peter Hamley (Global Head, External Innovation, Drug Discovery Platforms at Sanofi) and Peter Eckes (President of BASF Bioscience Research). Although these plenary speakers are from quite different backgrounds within the industry, they shared several overarching ideas on the topic of digitalization in precision medicine.
In this new era of AI, every organization has to imagine that they have a large “data lake” which they can explore and mine for data. Across the world, people are using deep learning algorithms to leverage and get value from big data in a much faster way. – Jonathan Berte, Robovision
Extracting value from big data
One common opinion was that AI and big data are vital for the future of healthcare. Given the sensitive information involved in the healthcare industry, there is obviously concern over digitizing elements of medical records. However, all three speakers agreed that the unprecedented amount of health data available for research opens up unforetold opportunities for progress in the health sciences.
This stance isn’t surprising, when we consider the work being done by the companies the speakers represent. Jonathan Berte, for example, explained how Robovision is using AI to extract valuable information from “unstructured” data such as images. One of their life science projects is the “rooting machine” which uses deep learning to teach itself how best to pluck and plant seedlings.
“This is really the core of deep learning: it’s an algorithm that creates itself,” Berte stated in his talk. “With deep learning we’re able to come up with filters that are auto-created from the data. If you look at the plant and health sciences industries, you don’t need to program your computer anymore. That is the core of our business: to create a platform for small and big companies where you can create deep learning algorithms yourself, without being a programmer.
“This idea is being leveraged right now in many different fields of expertise and industrial applications. We’re seeing relentless disruption tearing down all the ivory towers. In oncology, for instance, we see systems based on deep learning achieving results very near to the experts.”
“AI is transforming the way we can discover therapies and treat patients. We are using AI and deep learning to transform drug discovery, to help us to more efficiently design compounds in the future. – Peter Hamley, Sanofi
“In this new era of AI, every organization has to imagine that they have a large “data lake” which they can explore and mine for data,” he stated in his plenary talk. “Across the world, people are using deep learning algorithms to leverage and get value from big data in a much faster way.”
Read this previous BioVox article to find out about using DNA as a strorage device.
AI in drug discovery
Peter Hamley from Sanofi expressed a similar sentiment to Berte in his talk, which addressed the role of AI in pharmaceutical R&D. In his opinion, using AI to identify leads and candidate molecules can ameliorate the drug discovery process, which is currently woefully inefficient.
“AI is transforming the way we can discover therapies and treat patients,” he states. “We are using AI and deep learning to transform drug discovery, to help us to more efficiently design compounds in the future.”
Digitalization has changed the way we are doing bioscience. The fundamental thinking is still the same but instead of having iterative steps, we have more data-inspired experiments running in parallel that allow us to come to conclusions faster and allow us to solve problems that we couldn’t solve before. – Peter Eckes, BASF Bioscience Research
Hamley also commented on how digitalization is allowing patients to play a more active role in their own treatments:
“Life for a patient is a health journey. A pharmaceutical company, like Sanofi, should be there all the way, supporting a more holistic approach to healthcare.
Things are changing in terms of treatment: it is no longer about selling a drug, it’s about the patient journey. This is made possible by better and easier diagnoses; the “omics” revolution has really changed the R&D process, allowing us to shift to prevention and management healthcare.
This shift is driven by digital tools. Patients are becoming more active and really want to take control over their own destiny in terms of their healthcare. By allowing the patient to be part of that process, we also generate data which can actually help us as researchers to improve healthcare overall.”
Read more about remote clinical trials in this previous BioVox article.
The third Knowledge for Growth speaker, Peter Eckes from BASF Bioscience Research, summed it up nicely:
“Digitalization has changed the way we are doing bioscience. The fundamental thinking is still the same but instead of having iterative steps, we have more data-inspired experiments running in parallel that allow us to come to conclusions faster and allow us to solve problems that we couldn’t solve before.”
