Why I’m Learning Clinical AI?
Today marks the beginning of a new journey, not just in learning, but in thinking differently about the future of medicine. As a clinician, I’ve worked in high pressure environments, from wards to ED’s, making decisions with limited time and complex information and have often wondered, “Could machines help us think faster, better, and safer?”
So, here I am, exploring what AI can bring to medicine!
What really is Clinical AI?
Clinical Artificial Intelligence is NOT about replacing doctors, but augmenting us- helping us sift through enormous amounts of data to make faster, more accurate, and consistent decisions.
Despite very few of us knowing how AI works, AI is already transforming care – be it through a model reading chest X-rays like an expert radiologist, or identifying patients at risk of sepsis from early warning signs. And this is exactly what I’ve set off to learn.
Getting the facts right: No Tech Background Needed
I start today with zero background in computer science or analytics, and here’s what I’ve identified so far:
- AI needs Data:
- Just as we started off linking the dots between various parameters that we measured off from a patient, incorporated the history and test results to come to a conclusion, and used cases to train, AI trains in a very similair manner – using thousands of cases, learning from the data contained in vitals, labs, ECG’s, images, and clinical histories and case notes.
- AI uses Algorithms:
- Algorithms serve the same purpose to machines that clinical guidelines serve to doctors. Some algorithms are simple, where an alert goes off if the oxygen saturation falls, whilst others are complex, such as the use of deep learning for images.
- Clinicians are the Humans-in-the-Loop:
- Our job is to interpret, validate and ensure that AI fits into real clinical workflows. AI generates a signal, and we add meaning to it.
Case Study that Struck Me: AI Detecting Sepsis.
AI tools are now available and in use at some hospitals that are capable of flagging patients at high risk of deterioration secondary to sepsis hours before they actually deteriorate. These systems monitor patient vitals continuously and alert staff early. It is the insights that we clinicians act upon.
This just illustrated to me that AI is simply a tool, and we’re still responsible for it is used.
What does this mean for us clinicians today?
In this day and age, we as clinicians need to step up with regards to AI,, advocating tools that improve patient care, questioning black box algorithms that lack transparency, and bridging the gap between data science and bedside medicine.
The modern healthcare environment is in need of clinicians who understand both medicine and machines. Thats where the future of medicine is headed.
Whats Next…
Tomorrow, I’ll dive into the basics of data- how it is structured, where data comes from, and why clean data is the foundation of safe AI.
If you’re excited about where healthcare is headed, follow along.
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