Artificial intelligence

How Artificial Intelligence is Changing Radiology Pathology

The majority of radiologists focus on improving the patient experience, and AI will take care of the rest. But AI is also helping radiologists with many other things, such as coordinating information, identifying patients for screening and immediate interpretation, and standardizing reporting and characterizing diseases. Whether this technology is a game changer for radiology pathology is unclear. Read on to learn more. DoraysLab cover the key points and discuss how AI is changing the field.

AI can detect colorectal cancer

Artificial intelligence (AI) is a promising tool for detecting colon cancer. By improving the way a colonoscopy is performed, AI can identify polyps. The goal is to detect bowel cancer in its earliest stage, when it is most treatable. While most colon polyps are harmless, some may develop into a more serious form of the disease, known as colorectal cancer. Colorectal cancer is the second-most deadly type of cancer worldwide and is often life-threatening.

A recent study showed that a standard colonoscopy can miss as much as one-third of colorectal polyps, or polyps. A new system developed by Cosmo Pharmaceuticals aims to increase the detection rate of precancerous polyps in colonoscopies. Its AI is capable of detecting a majority of polyps. The company recently published the results of a trial using the GI Genius system, demonstrating that the AI can detect as many as 96% of polyps.

The AI system will rule out MSI/dMMR in a clinical setting, while also reducing the cost and turnaround time for molecular profiling. AI is also capable of detecting MSI/dMMR in routine pathology slides. However, further research is needed to assess the accuracy of AI-based MSI/dMMR detection. In addition to its low detection rate, the AI system also has a higher sensitivity than existing systems.

AI can detect left atrial enlargement from chest x-rays

In this study, an AI-based program was developed to detect left atrial enlargement from chest radiographs. Several conditions can cause the left atrial enlargement, including a variety of heart diseases. To help doctors detect the condition more accurately, the algorithm was taught about the left atrial wall by analyzing 12-lead electrocardiography. After training the algorithm, it tested its accuracy by detecting enlarged left atrium, which was the most commonly seen type of atrial enlargement.

Traditionally, a chest radiograph is the first imaging study available to a physician. The chest radiograph is an accurate initial screening tool for cardiovascular disease, but a radiologist’s visual assessment can be inaccurate. By using artificial intelligence, a machine-learning algorithm can quickly detect heart structures and identify the specific chambers contributing to the enlarged cardiac silhouette. The AI-based system was also tested on a patient database, enabling the creation of a standardized, reliable reference system for comparing the data from different imaging studies.

An AI-based ECG can identify individuals with LAE and identify groups of patients at risk for cardiovascular disease. Further refinement and external validation are necessary before this technology is used to screen patients for LAE. Nevertheless, this method holds promise as a screening tool for LAE. This study was supported by several other studies and a review article on AI-enabled ECGs

AI will replace radiologists

Despite the recent buzz surrounding artificial intelligence (AI) and radiology, the future of the field isn’t that bleak. While AI algorithms can interpret one exam better than a human radiologist, they are unlikely to be able to read other modalities or exams. However, this won’t prevent radiologists from making money and doing their jobs, so long as they train their systems well. That’s the key to success for AI.

According to the report published by the KSR, AI-empowered radiologists will be more empathic and creative. Their intuition and imagination will also be greatly enhanced. As a result, the future of radiology may involve expertise in teleradiology and web-based portals for patients. However, the future of radiology could be bleak if radiologists don’t embrace AI. Hopefully, these technological advancements will make radiologists even more valuable in the future.

For example, an AI algorithm that can correctly diagnose common chest conditions could be a valuable assistant for a thoracic radiologist. This way, AI can help them focus on detecting uncommon diseases and disorders. For example, an AI algorithm can recognize collapsed lungs in a chest x-ray. This may not be an urgent case, but the physician could spend hours sifting through non-urgent cases. This Article is by morain khan – he is a content writer at DMC- a digital marketing company in Jaipur. Feel Free to follow him on twitter and linkedin.

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