{"id":54789,"date":"2025-03-06T22:15:15","date_gmt":"2025-03-07T01:15:15","guid":{"rendered":"https:\/\/www.pixeon.com\/en\/?p=45039"},"modified":"2026-03-07T06:26:33","modified_gmt":"2026-03-07T09:26:33","slug":"artificial-intelligence-in-healthcare-examples-of-ai-for-radiology","status":"publish","type":"post","link":"https:\/\/www.pixeon.com\/en\/blog\/artificial-intelligence-in-healthcare-examples-of-ai-for-radiology\/","title":{"rendered":"Artificial Intelligence in Healthcare: Examples of AI for Radiology"},"content":{"rendered":"\n

It is already known that Artificial Intelligence (AI)<\/strong> is a powerful ally in the challenge of increasing companies\u2019 productivity. According to PwC\u2019s Global Artificial Intelligence Study<\/a>, the estimate is that this technology will increase global GDP by USD 15.7 trillion by 2030<\/strong>.<\/p>\n\n\n\n

The healthcare field is one of those that benefit from artificial intelligence. AI brings benefits both to institutions and physicians, as well as to patients.<\/p>\n\n\n\n

Its use is especially advantageous in radiology and diagnostic imaging clinics that deal with a high volume of requests for exams and reports<\/strong>, and whose application, little by little, is becoming a prerequisite for CDIs that aim to offer a Health 5.0 service<\/strong>.<\/p>\n\n\n\n

In this article, understand the role of AI in diagnostic medicine and learn some examples of artificial intelligence for radiology<\/strong>.<\/p>\n\n\n\n

The Role of AI in Radiology and Diagnostic Imaging<\/h2>\n\n\n\n

The main objective of applications with artificial intelligence is to enhance and support human activities<\/strong>, not to replace people.<\/p>\n\n\n\n

Therefore, the radiologist\u2019s work is not discarded, only improved.<\/strong> After all, a radiological diagnosis cannot be issued solely based on images.<\/strong><\/p>\n\n\n\n

Regardless of the specialty, the physician needs to evaluate different aspects before preparing a report<\/strong>, such as the patient\u2019s history, symptoms, existing pathologies, genetic and family predispositions, mental health, and even socioeconomic and environmental issues.<\/p>\n\n\n\n

AI tools cannot provide or sign diagnoses; rather, they can support professionals in recognizing and differentiating images through suggestions of clinical findings.<\/strong><\/p>\n\n\n\n

In this way, a radiologist needs to assume professional and ethical responsibility for the report, even with this support. Some areas benefit more from advances in technology.<\/p>\n\n\n\n

This is the case for complex exams<\/strong>, such as computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging. The same applies to organs that are more difficult to diagnose, such as lungs, breasts, and brain, and in certain specialties such as mastology and oncology.<\/p>\n\n\n\n

How Does Technology Support Radiologists?<\/h2>\n\n\n\n

With the use of AI, the expectation is that the radiologist improves time management by shortening manual activities<\/strong>, mainly with regard to quantification.<\/p>\n\n\n\n

With this, the professional dedicates more time to analyzing images and the patient\u2019s history, and less to operational activities.<\/p>\n\n\n\n

In the past, for example, to detect the coronary artery, it was necessary to perform segmentations manually. Today, the software does this\u2014it segments, characterizes atherosclerotic plaques, and even helps assign the stenosis grading.<\/p>\n\n\n\n

In addition, AI helps productivity and the quality of institutions\u2019 professionals\u2019 work.<\/strong><\/p>\n\n\n\n

Some AI algorithms often manage to quantify as well as, or even better than, the radiologist themselves. The variability of measurements performed by algorithms tends to be lower and, therefore, they ensure greater precision.<\/p>\n\n\n\n

Main Advantages of Using Artificial Intelligence Solutions in Radiology and Diagnostic Imaging<\/h2>\n\n\n\n

AI tools have been part of Radiology for quite some time<\/strong>, such as in bone densitometry analysis, voice recognition for preparing reports, and automatic segmentation of coronary arteries.<\/p>\n\n\n\n

There were significant gains when they were implemented, and now they go unnoticed because they are incorporated into our day-to-day.<\/strong><\/p>\n\n\n\n

More modern AI tools, with a major impact on radiological productivity, are still in the validation phase. Some benefits are:<\/p>\n\n\n\n