Unleashing the Potential: A Journey Through Technology and Medicine
Welcome to the first edition of my newsletter!
In this first edition, I will cover:
My journey exploring the synergy of technology and medicine
My background as an experienced physician-engineer with insights into the tech-enhanced medical world
The need to navigate hypes and understand disruptive technology, including large-language models and AI, which may transform healthcare
Let's explore the future of medicine and technology together: subscribe and discover the potential in innovation
Read time: 4-5 minutes.
Welcome to the first edition of Transforming Med.Tech, the newsletter where you can join me on my journey exploring the intersection of technology and medicine. My name is Matthijs Cluitmans and I am trained as a physician and engineer. I have experience in research for both academia and industry, and my passion lies in bridging the gaps between science, application, technology, and the clinic.
My motivation for embarking on this journey stems from the belief that there is an immense potential for technological innovation to enhance medical decision-making processes. And I truly believe we are at an exciting pivotal point, with recent rapid advancements in technology. Join me in delving into how technology is shaping medical decision-making and transforming healthcare for the better of patients, healthcare practitioners and society.
From excitement to reality: Understanding technology hypes
Over the years, I have observed the progress of various scientific fields. For example, artificial intelligence (AI) has undergone significant advancements. I studied it almost 20 years ago (welp), when it still felt very academic. But the speed with which it impacts the world recently has been unprecedented. Conversely, I have worked on Digital Twins, an innovative technology that has gained considerable recognition for personalizing treatment approaches, but which at the same time is complex to implement and until now fails to live up to the hype. So I've seen a few hypes come and go. I've found the "Hype Cycle" a useful way to think about this. This model, while a simplification of reality, offers valuable guidance on the different phases that disruptive technologies traverse before they reach mainstream adoption.
The Hype Cycle is a graphical representation of the maturity and adoption of technology over time. It was developed by Gartner, a global research and advisory firm.
The hype cycle has five stages:
🔥Technology Trigger: This is when a new technology is first introduced. There is a lot of excitement and speculation about the potential of the technology.
🤩 Peak of Inflated Expectations: This is when the hype cycle peaks. People start to believe that the technology will solve all of their problems.
😭 The Trough of Disillusionment: This is when people start to realize that the technology is not as perfect as they thought it was.
💡 Slope of Enlightenment: This is when people start to understand the limitations of the technology, but they also start to see the potential benefits.
🙌 Plateau of Productivity: This is when the technology reaches widespread adoption. It is no longer a new technology, but it is still being improved and refined.
Although the "Hype Cycle" is not an exact science, it helps us understand the general trajectory of emerging technologies. As these innovations progress through the cycle, inflated expectations give way to disappointment, with productivity arriving only later.
Will generative AI and large language models live up to the hype?
Consider the rapid development of generative AI and Large Language Models (LLMs), which are currently experiencing an unprecedented rate of uptake. Every day, my Twitter feed is flooded with new applications for these AI technologies, many of which make tremendous promises. Are we at the stage of inflated expectations? There definitely seems to be a tendency to think that LLMs will solve everything in the near future. Or are we at the stage of enlightenment? We are indeed understanding more and more about the limitations and potential of the technology, and the developments have been ongoing for a while.
Or perhaps we should take another model to look at AI's recent adoption, more akin to that of other disruptive technologies such as the internet, smartphone and social media. The disruptive S-curve is a model that describes the adoption of new technologies over time as characterized by slow initial growth (milk-bottle-sized mobile phones, anyone?), followed by a rapid acceleration (all my friends suddenly got a smartphone), and then a leveling off as the technology reaches its maximum potential.
The uptake of LLMs is following this pattern. In the "early" days, LLMs were very limited in their capabilities and were not widely adopted. Remember how personal assistants could answer your questions about the weather or point you to a Wikipedia article, but not much more? Their capabilities increased dramatically over the last months, most notably by the introduction of ChatGPT to a large audience, and later search engines/assistants Microsoft Bing and Google Bard. This has led to a rapid acceleration in the adoption of this technology: ChatGPT had 100 million monthly active users two months after its launch - faster than any other consumer application in history. If that isn't the steep upslope of S-shaped adoption...
A personal journey through the transformation of medical technology
Of course, this rapid advancement also presents challenges. Companies offering AI-driven solutions risk becoming obsolete overnight as newer, more potent AI models emerge. Industries with existing workflows need to think how they can adapt or adopt, or risk becoming outdated.
And the question remains: what do these developments mean for science and healthcare, and how will they impact our work? There's no doubt that advancements in technology, particularly AI, are beginning to reshape how we approach science and medicine. LLMs, for example, enable me to quickly and accurately summarize research papers before I decide to dive into them deeper and help me focus on what truly matters. They've also helped me to structure my ideas and provoke my thoughts by posing new perspectives. Soon, we can also expect significant changes in healthcare. However, these transformations in medical technology will come with challenges such as reliability, explainability, and regulatory compliance. Healthcare is conservative, and it typically takes time for new technology to be implemented there. When I did my rotations, everybody already had a mobile phone but we were answering in-hospital consultations with beepers (running to fixed phones to answer them), and kept on doing so for many years...
In this newsletter series, I'll be sharing my personal journey as I explore the technological advances that are redefining medicine. I'll not solely focus on AI - but quite naturally, with the rapid advancements, it may cover the majority of my first editions.
My goal is to provide you with valuable insights in transformations of medical technology, at a frequency that prioritizes quality over quantity. You'll be kept up-to-date with my latest findings and personal experiments as I navigate this evolving landscape. Don't expect daily updates on yet-another novel AI solution or the latest medical device, but anticipate gaining insights on mid-to-long term implications of technology developments. Just a few teasers: I've been exploring how to benefit from the synergy between 'fuzzy' generative AI and 'exact' biophysics models for disease insights, how to improve my teaching of medical consultation skills, and how to better understand the substrate for arrhythmias.
If these topics intrigue you, I invite you to subscribe to this newsletter and join me in discovering the future of medicine and technology. Let's unravel the potential of these groundbreaking innovations and transform healthcare for the better.