Ideas are not harder to find. Opportunities are.

Here’s an interesting post. Something that resonated instantly with me. Ideas are not harder to find. Opportunities to play them out are.

While pure play clinical research was completely out of league (and I don’t believe in rehashing old ideas or reworking them to have them published elsewhere), I experimented with many “non-clinical ideas”; first build, iterate and then put them into production. As such, I gained incredible insights into the interesting overlap of technology with medicine, which has put me in a unique position. First read this:

Ideas aren’t getting harder to find and anyone who tells you otherwise is a coward and I will fight them

This is how science works, too. To do science, you don’t need to start with the dawn of all human knowledge and then work forward. You start with the current state of knowledge and go from there. Learning the history of science is helpful for shaping your intuitions and giving you perspective, but you don’t actually have to read Darwin, for example, to do evolutionary biology.

That’s why I’m puzzled by the claim that scientists must labor under an ever-increasing burden of knowledge. The author of that paper writes: “If one is to stand on the shoulders of giants, one must first climb up their backs, and the greater the body of knowledge, the harder this climb becomes.” This suggests that if you peek into PhD programs, you’ll see lots of students bent over their books, desperately trying to learn everything that’s come before so they can start their own projects. “I can’t do any physics yet,” you might hear them lament. “I’m only up to Huygens!” Instead, you’ll see PhD students doing original research from Day 1—and often long before. Indeed, many students start doing more interesting work once they stop looking at lots of previous work, as it finally frees them from imitating other people and searching for “gaps in the literature,” two strategies that are unlikely to yield anything interesting.

emphasis mine

I didn’t start off by learning complex coding sessions. Instead, I had to create a language to translate my vision into something the coder could understand. I learned how to sketch the blocks for a mobile application (for example), and that process is called “wireframing”. I learned how difficult it is to launch an app in the App Store and get people to use it. I learned about App Store economics, its “review” processes, and the underlying technology of applications (as a browser frontend) to display notifications and content. I learned about UI (user interface) and “dark-patterns” to improve “interaction with the application” and to cut costs effectively, utilise Telegram through bots. I learned about the API’s and limitations of the Bot API’s in Telegram.

All of this didn’t require me to leave my clinical practise. It was all part of the cumulative learning process, where I learned through trial and error (and made many mistakes in the process). I learned to overcome the fear of failure and rejection. I attempted to put this through a formal process of a PhD, and it revealed more about the current state of academia.

Another revealing incident was related to an interview I appeared for, which was related to an interesting radiobiological question. I can’t get into specifics, but I wasn’t selected because it would have required a formalisation of my appointment, which they weren’t prepared for. They wanted me to slog instead. Nevertheless, I had shared the implications of research, publication pathway and application in other clinical domains. The email remain unanswered, though. I respect their point of view (and original research idea), but the details of the project were shaky until I formalised the clear application in the clinical domain. I rely on combinatorial matrices, and since I focus on extensive reading, I can easily move in and out laterally/horizontally to assess the credibility of the proposal.

Therefore, clear line opportunities are lacking, which can encourage lateral thinking processes (and some sprinkling or “neurosciences/meditation”) to get insights. We are not short of the research ideas. The blog is dedicated to finding potential applications, and if you read it carefully, you’d find enough ideas to get a breakthrough.

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