Written by Dolcy Dhar, Head - Healthcare AI @CellStrat
As a healthcare professional, the decision to transition into an AI role was daunting. When I embarked on my AI journey, I quickly realized that every scientific discipline has its own nuances and “cheat codes.” Current research suggests that mastery doesn’t necessarily require 10,000 hours of practice; sometimes, just 20 hours can be enough to grasp the foundational elements of a new topic.
Cheat codes are an invaluable tool for uncovering these essential insights, much like focusing on key algorithms to understand the core principles of an entire class of algorithms. One such “cheat code” I’ve encountered is particularly relevant to healthcare professionals who may find themselves overwhelmed by the perceived complexity of AI and intimidated by coding. Consider the complexity of AI as analogous to the complexity of the human brain. Both are intricate systems driven by computations. In AI, these computations are based on assumptions encoded in binary (0s and 1s), which are then translated through mathematical rules into software processes. In the human brain, computations are governed by chemical rules, with the genetic code functioning similarly to binary code.
In AI, the hardware supports software components that interact based on these computations. Similarly, in the human brain, neurons (cells) are the physical space that houses genetic code, which translates into proteins. These proteins, much like software components, work together or individually to create meaningful actions.
We can extrapolate this analogy to the level of AI. Software components build on top of each other to develop complex architectures that handle various tasks. This complexity gives rise to different forms of intelligence, such as audio intelligence, language and text processing, and image and vision analysis. These are all built with interdependent components designed to achieve specific tasks. Similarly, our brain is divided into different lobes, each responsible for distinct functions. For example, the occipital lobe manages all aspects of vision, often supported by other forms of intelligence like audio and language processing, which help provide meaning to what we see.
Understanding these parallels can help demystify AI for healthcare professionals and make the transition into this field less daunting.
I hope this perspective brings value to you as it did to me.
