Unveiling the Symphony of AI and Radiology: Decoding Medical Imaging Analysis

Beep. Beep. Beep. 

You lie on the MRI table, surrounded by the soft hum of the gigantic machinery. It’s an ordinary moment for many, but have you ever wondered about the role that these machines play in your health care journey? These images hold the key to understanding what’s happening inside your body, and they’re not just pictures — they’re a radiologist’s jigsaw piece for solving the puzzle of your health. And in this modern health care narrative, a new kind of ally has emerged — one that’s not only a detective but a puzzle-solving prodigy — artificial intelligence. 

But what exactly is it? AI, or artificial intelligence, is a field of computer science focused on creating machines that can perform tasks that typically require human intelligence, such as learning, problem solving, and decision making. This “new” technology has actually been a hot topic for years. With the recent implementation of AI in people’s everyday lives, like ChatGPT, Siri, or even the Snapchat AI in many teenagers’ phones, it’s resurfaced again. AI is seen in almost every aspect of our life — social media, online shopping, and navigation apps on our phones! 

However, within the broader landscape of AI’s influence across various aspects of our lives, one intriguing domain where AI is gaining ground is within the health care sector, particularly in the field of radiology.

Radiologists, before the help of AI, would have to meticulously analyze countless images, looking for the smallest clues, like detectives examining evidence at a crime scene. Their expertise and dedication have always been essential, but the sheer volume of medical images generated daily can often be overwhelming. This is where AI comes in. Unlike human radiologists, AI can learn and process significantly more information when well-trained, and it doesn’t get tired! This is called medical imaging analysis — where AI can easily interpret meaningful information from medical images, which can help diagnose, monitor, and plan the treatment of various medical conditions. 

Building on the potential of AI imaging analysis, Dr. Bibb Allen, chief medical officer for the American College of Radiology Data Science Institute, is one of many radiologists who have delved deep into the role of AI in radiology. His work illuminates the promising path that AI is paving within this field. It’s a journey where innovation meets necessity and as Allen puts it, “I just see AI as being transformative of what we [radiologists] can do.” 

But although this imaging analysis may seem flawless, it isn’t foolproof. Work still has to be done with AI to perfect it. 

First of all, AI still has to be trained before being put to the test, in a real hospital with real cases. If the AI isn’t exposed to a large enough demographic of diverse cases, it may only work in a few circumstances, which would not be as helpful and may cause the AI to make more mistakes. To train the intelligence, Allen studies the use of structured-use cases — specific scenarios/applications for which AI is designed and applied. 

Structured-use cases are a type of case fed to the AI so that it can learn how to solve a variety of problems. According to Allen, to predict the problem more easily, it is important for the AI to be exposed to all of these problems. However, one threat to this is the tendency for institutions to use their own data, rather than feeding the AI other institutions’ data (or, more commonly, other institutions dislike the idea of giving away their own hard work on collecting data for another institution’s analysis). Without this crucial data, discrepancies and biases begin to form in the AI. 

“For example, the AI in Alabama might be more geared towards finding health problems related to something like obesity – compared to other states like Colorado, where [the program] may not be so proficient because maybe more people go hiking or go outside more,” Allen states. These different population demographics can negatively influence AI analysis — thus, the need for balanced structured-use cases is critical. 

Another limitation is the fact that all AI will eventually break down – some human mindpower is still required to help calibrate the AI and keep it working in mint condition. Due to the fact that AI is in the field of health care, and specifically radiology, there must be little to no room for error because the resulting diagnoses are literally the basis of a patient’s health care. With an incorrect diagnosis or the AI potentially making a critical mistake, the hospital could lose money and the patient’s sickness can go misdiagnosed, or, even worse, undiagnosed.  

So, although the use of AI is opening doors to precision and efficiency that were once incomprehensible, there are still a few issues that need to be addressed. But these setbacks are nothing compared to the general growth and presence that AI has within the radiology world. 

Next time you’re in the MRI machine, remember that the symphony of beeps, pops and rumbles in the room is just a prelude to the harmonious fusion of technology and human expertise that could now be radiology — where AI and radiologists work hand in hand to decode medical mysteries.  

Beep. Beep. Beep.

The story has just begun.