How would you feel if an algorithm could inform you that you had cancer-based on your mammography exam or CT scan? It is highly likely that in the future, the creative work of radiologists will be necessary to solve challenging problems and to oversee diagnostic procedures. AI will absolutely become part of their routine in diagnosing basic cases and helping to assist with repetitive jobs. So, instead of feeling threatened by AI, radiologists need to become familiar with how it could help them in their daily lives for the better.
Radiologists who use AI will replace those who don’t
AI is surrounded by fear and the future impact it will have on medicine. Indicators suggest that the world of healthcare will be revolutionised by AI. The advancements in deep learning algorithms and narrow AI have creating a buzz around the medical field of imaging something that has set many radiologists into a panic. Curtis Langlots, Professor of Radiology, recently presented at the GPR Tech Conference in San Jose. He mentioned how one of his students had sent an email saying they were considering going into radiology but that they weren’t sure it was a viable career any longer. This is completely wrong, radiology isn’t a dying profession, in fact, it’s far from it.
There is hype around the radiology profession that deep learning, machine learning and AI, in general, is going to replace radiologists in the future and that perhaps all radiologists will end up doing is looking at images. However, its simply not true. That is the same as saying pilots could eventually be replaced by planes flying in autopilot. Autopilot may be useful, but pilots will always be needed when rapid judgement is required. So, the technology and human combination is a winning one, and it’s going to be the same in healthcare too.
While it may be true AI will not replace radiologists, it must be said that radiologists who use AI will certainly replace those who don’t. Here is why:
What do X-ray lamps, cat intestines, and the history of medical images have in common? The clinical radiology field began with the discovery of the X-ray back in 1895 by a German man named Wilhelm Conrad Röntgen. X-ray mania had taken over the world within the two months following its discovery. With headlines such as “new light seeing through flesh to bones” and “soon every house will have a cathode-ray machine” it really was a revolution. Perhaps other similar hyped technologies are brought to mind?
Excited by the discovery, Thomas Edison wanted to try to create a commercial “X-ray lamp” – his efforts failed, sadly. As did the efforts to try to get an X-ray of the human brain. He allowed story-driven journalists to go nuts as they were reported to have waited outside his lab for weeks waiting for the latest innovation. Some reporters created fake images of the human brain, one of which was a pan of cat intestines by H. A. Falk radiographed in 1896.
Even though some of the earlier methods turned out to be impossible projects, X-ray soon found its groove in medicine. And it is expected that the same will happen with AI and medicine soon – hopefully with no cat intestines this time!
Radiology has been used in technological developments since it was first introduced. The Knick, a TV series that depicts the first era of modern healthcare, an inventor in the series contacts the hospital manager to present him with his innovation: the X-ray machine. It took around one hour for the machine to take the picture. Nowadays, if you go to the hospital for a check-up on your lungs, it takes only a couple of minutes for the x-ray procedure to take place, and only a few more minutes to get the results.
A lot has changed since the very first experiments with the “X-ray lamp,” however, one thing remained constant – rapid technological advancements in radiology.
Larger range of tools and better precision
The x-ray was discovered around half a century before ultrasound. These new commercially available systems allowed for wider dissemination from the mid-sixties onwards. With growing advancements in piezoelectric materials and electronics, improvements were made from bistable to grayscale images. Also, from still pictures to real-time moving images. The move from room-sized huge ultrasound machines to portable ones was amazing to see, and it only changed over another half a century. With Clarius Mobile Health introducing the very first pocket-sized handheld ultrasound scanner complete with a smartphone app, physicians can carry it around with them to undertake fast exams and to guide quick procedures like targeted injections and nerve blocks.
In 1997, the first CT scanners were developed. They had a single detector for brain studies and were created under the leadership of Godfrey Hounsfield. An electrical engineer and EMI (Electric and Musical Industries Ltd.) Raymond Damadian built the very first MRI scanner by hand in the 1970s, with the help of his students at New York’s Downstate Medical Centre. The first MRI scan of the human body was completed in 1977, and a human organism with cancer in 1978. By the 2000s medical imaging was routine in many centres, such as fetal imaging, body MRI, cardiac MRR, and functional MR imaging.
With precision comes automation
With expanding means in radiology come an increase in precision. Precision is still the focus, there has also been a shift towards automation which aims to make radiologists’ lives easier. Radiologist shave to look through many images every day, so it’s inevitable that this part of their job can be automated. Algorithms can be trained to find and detect various abnormalities based on the images. So why not allow them to do this job, so radiologist scan spend their time on more complex issues.
With the possibilities of deep learning, algorithms can teach themselves while radiologists oversee its effectiveness. The longer it is used for, the more effective it will be and it’s an opportunity too good to miss. Radiology could fast become one of the most creative specialties where problem-solving and the holistic approach is key.
However, this does not mean that AI will take over all radiologists’ tasks. There will be common findings and diagnosis on medical images that AI can help with, however, there are also very uncommon problems that we simply cannot miss. It could be hard for deep learning to identify those issues. So, at what stage is this technology now?
Is it possible for AI to predict when you might die?
Scientists have carried out experiments at the University of Adelaide where AI systems have been used to see if they can tell when a person is going to die. The deep learning algorithms have been analysing CT scans of 48 patients to predict whether they might die within the next five years, the study so far has been 69% accurate. This is a similar result to human diagnostics, which is an impressive achievement. The deep learning machine was trained to indicate signs of disease in the organs by using a series of 16,000 images. The aim of the research is to check and measure overall health, rather than to identify a single disease.
This is just the tip of the iceberg, however, there is a lot of research being carried out to teach algorithms about different diseases and how to detect them. IBM launched and algorithm called Medical Sieve has been able to assist clinical decision making in cardiology and radiology. The system can look at radiology images and detect problems faster and more reliably.Watson, an IBN AI analytic platform, is also used in the radiology field. Following the purchase of Merge Health in 2015, Watson gained access to millions of radiology studies and a lot of existing medical data which enabled brilliant training of the AI functionality and therefore gave better readings at imaging exams.
Other giants such as Agfa, Siemens, and Philips are already working with AI integration into their medical imaging software systems. GE is working on a predictive analytics software that utilizes AI. It helps in imaging departments if fails to show at work due to sickness, or if the volume of patients increases. With similar work-in-progress in predictive analytics software for imaging is tech company – Vital. There are also many smaller and larger start-up companies that are harnessing the power of AI for radiology.
This research does not mean thar we are currently ready to have patients face their life expectancy based on medical images, however.
What are the challenges in introducing AI to the radiology department?
To gain an idea of when machine learning may be introduced on a wider scale, we first need to see hoe machine learning currently works in radiology. The usual process is: The algorithm is fed by many images and data parts that allow it to learn and detect differences in tissue. Just like how computers can recognize images of cats and dogs. If the algorithm makes an error, it is spotted by the researcher, and they make an adjustment to the code. It is therefore a lengthy process and tons of data is needed. It is believed that the result will look like this: Radiologists will conduct the high-level exam, and the algorithm will likely create a minable, structured, prelim report. The algorithm will therefore do the quantification that most humans don’t enjoy doing, and it will do it very well.
There is also some convincing to be done to show hospitals that AI algorithms really work. Experts suggest that there will be a process that takes advantage of external and internal “crowdsourcing” of appropriately anonymised data.
For instance, a user could have established data science algorithms that are based on anonymised datasets from their hospital network. Then, a new hospital could use the algorithm to further refine the anonymised local data to customise it for their needs. Once hospitals have seen “a win” scenario, they may be encouraged to allow systems to allow the systems to use further datasets to contribute to the user’s solution. It’s like how we try to go into cool water on a hot summer day. Firstly, you see other people doing it, then you see that it’s safe, and so you get involved too, perhaps dipping your toes before fully committing.
When will we get to have AI analysing out CT scans?
We edge close to clinical use every day. The Data Science Bowl of 2017 aimed to detecting lung cancer by using smart algorithms on more than 1000 anonymous lung scans that were provided by the US National Cancer Institute. There were over 18,000 unique algorithms created during the challenge. The main goal was to find the path to deliver the algorithms to systems that can be used in clinical care, and therefore members like the FDA and the American College of Radiology can connect to the image system users and the radiologist who would use these algorithms.
In 2017, the FDA went on to approve the first cloud-based deep learning algorithm. It was developed by Arterys for cardiac imaging. We are slowly getting there, experts suggest that within the next three years, we should see many machine learning algorithms being used in clinical pilot schemes and in approved use. Also, it is expected that in this timeframe, there may be low does CT lung cancer deep learning algorithms within the arsenal of a radiologist’s toolkit. This would be able to assess an individual’s risk of lung cancer.
However, there are no concrete estimations, and it will be a step-by-step process where a lot of subfields will be developed faster than others. An example of this is mammography, where it is more likely that AI will be used sooner than in CT scanning. There is potential for a quicker approach that could see preliminary reports within the next 10 years.
The future of radiology is with AI
Experts and research trends show how AI will revolutionise the future of radiology. The medical field need to adopt it rather than fun aways from it.
Instead of radiologists shouldn’t feel pushed out by machine intelligence, they should engage with it, learn it, and promote it. AI is something that has the ability to0 help patients and therefore should be welcomed with open arms. There will be huge changed within the radiology field in the upcoming years. The field needs to be kept at the forefront, and what matters most is taking care of the patients.
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