AI could take medical imaging to the next level
Artificial intelligence can support radiologists and pathologists to diagnose a wide variety of conditions through advanced analysis of medical imaging
The automatic analysis of medical images is today a topic of great interest for the scientific community. This analysis can provide clinicians with decision support, with the ultimate goal of guaranteeing patients an increasingly objective and personalized treatment path. Artificial Intelligence may provide support to clinicians during the actual clinical and surgical procedures by helping them better diagnosis critical deseases.
AI technology in medicine is growing at a rapid clip, and imaging is leading the way.
AI assistance is already a thing in mammograms, for instance but an early detection of breast cancer is not the only goal in this scenario. Doctors are using the technology to scan X-rays of people’s chests, ultrasound videos of infants’ hearts and more.
Scenario
Artificial intelligence and machine learning have captivate the healthcare industry as these innovative analytics strategies become more accurate and applicable to a variety of tasks. AI is increasingly helping to uncover hidden insights into clinical decision-making, connect patients with resources for self-management, and extract meaning from previously inaccessible, unstructured data assets.
Medical imaging data is one of the richest sources of information about patients, and often one of the most complex. With megapixel upon megapixel of data packed into the results from X-rays, CAT scans, MRIs, and other testing modalities, combing through extremely high-resolution images can be challenging even for the most experienced clinical professional. Artificial intelligence has already proven that it may be a valuable ally for radiologists and pathologists looking to accelerate their productivity and potentially improve their accuracy.
AI can rely on massive amounts of data from medical imaging
A radiologist job takes training, focus and attention to detail — but even experts can make mistakes. A radiologist’s daily error rate may be around 3 to 5 percent, researchers have found. Such errors tend to stem from being overworked, scientists reported in 2023 in the European Journal of Radiology.
The problem isn’t likely to disappear any time soon. More people are getting scans, which means doctors are acquiring more images than they used to, and the number of pixels in each image is also growing. For radiologists searching for a suspicious spot on an X-ray, the needle in a haystack analogy doesn’t quite cut it. It’s more like “looking for a needle in a haystack under immense time pressure when the patient’s health is on the line, when you have legal liability, and you really don’t want to miss anything”, said a leading scientist to ScienceNews.
What can be immensely challenging for humans may be a ripe opportunity for AI. With massive quantities of high-quality digital images, scientists can train AI computer models to seek out specific features in a person’s scan, like a smudge in a breast image or signs of lung disease on an X-ray.
Such models may help improve radiologists’ accuracy and efficiency, and even flag images that look most alarming so doctors can triage cases based on which ones may need immediate attention. When Langlotz sees his cases piling up in the morning, for example, “I’d love to know which of those is more likely to have a problem,” he said.
AI may help doctors spot signs of disease
When a person gets a chest X-ray, a radiologist may be checking for lung cancer or signs of an infection. But within that image, details about other aspects of a person’s health are hiding in plain sight. AI models have the potential to squeeze more information out of each scan. There’s a richness to image data that isn’t being mined by radiologists.
Take cardiovascular disease. Doctors usually calculate a person’s risk by factoring in information like cholesterol levels and blood pressure. But those calculations can’t be done if data are missing. That’s where AI could step in — by gleaning cardiovascular information from routine X-rays.
Using a set of previously collected images and follow-up data, an AI model scanned chest X-rays from nearly 8,900 people ages 50 to 75 and identified who might later experience a heart attack or stroke, researchers reported in the 2024 April Annals of Internal Medicine. It’s an example of “opportunistic imaging,” where AI trawls images for medical clues beyond the original purpose of the X-ray. The roughly 4,200 people flagged by the AI were 1.5 times as likely to have a serious cardiovascular problem over the next 10 years as other people evaluated.
Such an AI tool could help people who lack access to regular health care, the study’s authors point out. During an emergency room visit, for example, these people might one day receive a routine chest X-ray that could flag issues other than the one that brought them to the hospital.
Besides, there’s another advantage in using AI: it may help examining how people’s medical images change over time. There’s this whole clinical history that needs to be accounted for, for each of us.
AI it’s not going to replace doctors any time soon
Artificial intelligence can help doctors to better spot disease, but it’s not taking over their role. In health care, putting new technology out into the real world is no easy feat. Scientists may publish reports of AI models that can identify malignant cells in brain cancer samples or parasitic worm eggs in children’s poop, but that doesn’t mean the technology is ready to go.
AI models need to be validated and tested in settings that go beyond their training. They need people with the clinical and technical know-how to use them. They need to go through the regulatory process for approval. And ideally, they’d be deployed in the real world and evaluated on their performance.
Using AI tools thoughtfully and carefully could ultimately make health care more efficient. AI is not magic, but it does have the potential to solve some problems. By easing workloads, for example, AI could give doctors more time with their patients.
We all hope AI can help doctors spot patients’ cancer earlier, reduce false positives and triage doctors’ work. How widespread those potential benefits are will depend on just how good AI can get, how clinics use the technology and who has access to it.
All in all, iIf there is a technology that can help find a small cancer, we don’t want to deny that to someone.
source: Science News I Scuola Superiore Sant’Anna
cover image: Stanford.edu blog
author: Barbara Marcotulli
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