In radiology, traditional medical imaging techniques are failing patients and providers — the rise of false positives leads to unnecessary treatments and malpractice lawsuits. Radiologists need an innovative alternative to turn things around. Is artificial intelligence the solution they’ve been waiting for? Traditional Imaging Techniques Must Improve Radiologists use X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRIs) to see what’s inside someone’s body. Once they get their hands on the images, they review them for potential signs of injuries, anomalies, or diseases. For the most part, radiologists rely on their expertise to identify suspicious results. With all of their training, you’d expect them to nail it every time. In reality, the on average. Almost three in 10 people will receive a false positive or negative. diagnostic error rate is up to 26% People who receive false positives will undergo unnecessary treatments. Usually, they’ll be overexposed to radiation and contrast agents during the imaging process as professionals try to find out what’s wrong. As a result, patient outcomes worsen. Even when radiologists get it right, their techniques still take time. After all, they manually. You can probably understand why the process can take so long. have to review thousands of medical images As you might expect, medical imaging in radiology needs a fast, accurate solution to its problems. AI is one of the few modern technologies capable of addressing each one of its needs while improving interpretation and diagnostic accuracy. What is AI’s Role in Radiological Image Interpretation? AI’s first application involves image review. It uses extensive training and leverages large datasets to rapidly evaluate X-rays, CT, and MRI scans. It than manual search methods and simultaneously compare thousands of previous cases. can locate images faster Another application involves quantitative results. When you study medical images, you look at the size, shape, density, and texture of bodily structures, which takes time and may be inaccurate. AI rapidly analyzes them, enabling it to provide accurate measurements. You’re probably already aware of how AI excels at identifying trends. It does a fantastic job of interpretation and diagnosis because abnormality identification revolves around pattern recognition. It can pick up on hidden details you aren’t able to notice. Radiologists often consult with other medical professionals when they find themselves stuck on a case. A generative algorithm might not be the same as a doctor, but it can still provide tips and suggestions. AI-driven collaboration is slowly becoming more accepted. How Does AI Help With Medical Imaging? AI improves medical imaging interpretation and diagnostic accuracy in numerous ways. 1. It prevents human error. As you know, AI is immune to human error. Where you might make a cognitive, search, or recognition mistake, an algorithm remains accurate because it relies on data and logical processes. It won’t overlook a slight anomaly or work off a misguided hunch. 2. It constantly improves its approach. Deep learning and machine learning algorithms constantly improve. They use real-world data to become more accurate over time. The longer you use AI for medical imaging, the more sensitive and precise it will become. 3. It makes data-driven suggestions. AI can improve interpretation and diagnostic accuracy by giving you suggestions. For example, it can recommend re-reviewing a suspicious-looking area you initially overlooked. Its data-driven insights help prevent you from providing false positives or negatives. It reviews large databases immediately. If you’ve ever used AI, you know it’s fast. You can use it to reference thousands of images in a massive database almost immediately, saving hours of searching and reviewing. Your diagnosis becomes more accurate since the algorithm knows exactly which references to pull. Benefits of Using AI in Medical Imaging There are numerous benefits of using AI in medical imaging. For one, patients receive faster intervention. Since algorithms speed up virtually every step of the process, they get their diagnostic results earlier and can move forward with treatment sooner. AI is also more accurate than humans are. In one study, researchers used a neural network to review images of brain tumors. Their on average. It correctly identified tumors and distinguished between different types almost every time. model achieved 98.5% accuracy Patient monitoring is another intriguing benefit of using AI in medical imaging. Since algorithms can detect minuscule changes in size, shape, density, and texture, they can tell you exactly how and where your disease progresses. All these minor benefits add up to the most significant one — better patient outcomes. When radiologists use AI, the rate of false positives decreases, and your survival rate increases. Also, since early detection becomes possible, you get a better chance of getting help in time. AI improves patient outcomes and shortens review times. Radiologists probably feel like a weight is lifted off their shoulders. Since they get more done with less effort, they experience burnout less often and can prioritize especially tough cases. Is AI Better at Radiology Than Humans? Technically, AI can identify and diagnose diseases way better than humans. However, radiology is more than that — it also involves intervention. While an algorithm can suggest a treatment plan, it obviously can’t step in and provide one. Even though AI improves virtually every aspect of medical imaging in radiology, humans are still an essential part of the process. You’re capable of abstract reasoning, contextualizing, and out-of-the-box thinking — all things an algorithm can only dream of. As it stands, AI is an essential part of the process. This fact is especially true given radiologists in the United States than new professionals can replace them. Radiology professionals should leverage algorithms in support roles. are retiring way faster Is AI Transforming Radiology? Algorithms are overhauling how radiologists’ work flows. They’re changing how people organize, evaluate, and search for medical images. In 2021, were already on the market. This technology may become a staple in this field and permanently transform it. over 150 AI-powered radiology tools AI Might be the Future of Medical Imaging in Radiology Considering dozens of AI-powered radiological tools already exist, there’s no telling how radiology will evolve. Still, all indicators point to algorithms becoming a permanent part of radiologists’ workflows. In time, you may see this field transform into a modern wonder. Sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545608/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571277/ https://www.uperform.com/blog/ai-training-in-healthcare/ https://www.nature.com/articles/s41598-022-19465-1#Sec9 https://radiologybusiness.com/topics/healthcare-management/healthcare-staffing/mismatch-between-radiologist-shortages-rising-exam-volumes-growing-concern-medical-imaging https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537124/

How Does AI Improve Medical Imaging Interpretation and Diagnostic Accuracy in Radiology?

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

3 Ways CFOs can Prevent Phishing Attacks

AI in Healthcare: Personalized Meds, Data Security, Virtual Nurses, More

Medical Image Retrieval Needs a New Benchmark

Building a CBIR Benchmark with TotalSegmentator and FAISS

Comparing Six Deep Learning Feature Extractors for CBIR Tasks

How AI Retrieves Anatomical Structures Using Vector Databases

3 Ways CFOs can Prevent Phishing Attacks

AI in Healthcare: Personalized Meds, Data Security, Virtual Nurses, More

Medical Image Retrieval Needs a New Benchmark

Building a CBIR Benchmark with TotalSegmentator and FAISS

Comparing Six Deep Learning Feature Extractors for CBIR Tasks

How AI Retrieves Anatomical Structures Using Vector Databases

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps