{"id":58418,"date":"2025-08-06T11:22:13","date_gmt":"2025-08-06T05:52:13","guid":{"rendered":"https:\/\/www.techjockey.com\/blog\/?p=58418"},"modified":"2025-12-12T12:04:22","modified_gmt":"2025-12-12T06:34:22","slug":"ai-in-radiology","status":"publish","type":"post","link":"https:\/\/www.techjockey.com\/blog\/ai-in-radiology","title":{"rendered":"The Impact of AI in Radiology on Modern Healthcare"},"content":{"rendered":"\n

A radiologist with years of expertise could still scan an image in minutes or a few hours. But the ones who are in the beginning might need the help of an expert.<\/p>\n\n\n\n

Delays in radiology mean risking a life.<\/p>\n\n\n\n

These delays are due to the time-consuming process of scanning an image without the scope of any error. But still, some studies show that diagnostic error rates in radiology range from 3% to 5% globally. The percentage is less, but it’s a serious risk for hospitals and patients alike.<\/p>\n\n\n\n

AI in radiology is the latest tech to spot these errors. These tools can spot diseases faster, often before symptoms even show up.<\/p>\n\n\n\n

This blog will discuss the intersection of artificial intelligence and radiology, covering its history, working, benefits, and real-world use cases.<\/p>\n\n\n\n

Let\u2019s decode.<\/p>\n\n\n\n

<\/span>Brief History of Radiology & Medical Imaging<\/span><\/h2>\n\n\n\n

In 1895, Wilhelm Roentgen was experimenting with cathode ray tubes when he accidentally discovered X-rays. Within weeks, doctors started using them to look inside the human body. It was where modern radiology began.<\/p>\n\n\n

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For decades, imaging was film-based. Radiologists developed and read scans manually, which was slow and required high expertise. Then came digital radiology in the 1980s. At that time, scans could be stored, shared, and reviewed on computers.<\/p>\n\n\n\n

The 1990s welcomed PACS – Picture Archiving and Communication Systems. This helped hospitals store massive volumes of imaging data securely and access it remotely. It laid the foundation for large-scale diagnostic workflows.<\/p>\n\n\n\n

The early 2000s saw the rise of computer-aided diagnosis (CAD) tools<\/a>. These were basic algorithms that flagged potential issues in mammograms or lung scans. But they lacked intelligence and needed a lot of human supervision.<\/p>\n\n\n\n

Today, we are moving toward machine learning and AI. Imaging tools are becoming smarter, faster, and more accurate with the incorporation of AI.<\/p>\n\n\n

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\"Timeline<\/figure><\/div>\n\n\n

<\/span>Emergence of AI in Radiology<\/span><\/h2>\n\n\n\n

The path of AI and radiology is built on a series of scientific leaps:<\/p>\n\n\n\n