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Trends in radiology technology in the near future

Can there be remarkable innovative trends in radiology technology in the near future? Yes, but more in the transformative change that would change the way of usage and workflows in radiology health services.

Currently, industries are extensively doing research on miniaturizing the devices; however, some challenges are still posed by a few technologies., like CT and MRI, which are huge in size and seem intimidating to patients as they are prepared for a scan. What has changed drastically is the Digital Transformation of images. Yes, today, we see that digital imaging technology has captured the market due to its quality, access, visual diagnosis, storage and sharing.

Innovative technology is poised as a solution to the current cumbersome workflow or for ease of reach and affordability, like in using MRI or CT scanners. Can there be a mobile imaging system, which can be moved toward the patient with a minimal risk of radiation or with no radiation? If the patient does not hold his breath while taking exposure, do we need to repeat? Can the dose be optimized to a safe limit?

As one of the solutions, there is a need to research the narrow-focused X-ray beam, where X-ray is focused electronically on the region of interest in both vertical and horizontal directions. Just an X-ray beam would not make a drastic change unless it is used along with high sensitive and high resolution detectors.

The key is to establish synchronization among elements of the imaging chain so that only anatomies which need to be radiated get exposed sparing the rest of the environment.

The hardware system needs to be complemented by simple acquisition workflow, ease of use, computer-aided vision improvisation, and finally the AI in analytics and predictions. AI can never replace radiologists, but it may become part of their routine diagnosis, aiding in quick decision and reporting processes. There are many cases where humans require assistance in deciding, and AI technology can be that assistance which makes humans efficient and effective.

AI continues to penetrate the medical field and has the capability to learn while in use. In medical imaging, AI can be trained to spot anomalies in human tissue and organs. Experience of hundreds of man-years can be packaged through machine learning to use hundreds of human experts to train the model. The result can be magical.

The big advantage of AI is its ability to process data at a very high speed without missing the smallest detail once the model is accurately trained. Now, with wearable technology creeping into our lives, it is possible to collect data at all times and send the data to the cloud for AI to process it further. This can be a life saviour for patients with critical ailments who require constant monitoring for any medical interventions.

The radiology field is advancing with 3D image augmentation and tissue simulation. AI aids in automatically segmenting anatomy parts for post-processing applications with anomaly detection. Radiologists’ involvement in AI model training enhances model reliability. Machine learning, fueled by extensive medical databases and improved computational power, drives current solutions.

A few more technologies are on the cards, like flexible detector, liquid nano crystal imaging, nano technologies, software services and data analytics. Future technology is poised to be less toxic, less harmful, high speed, and safe to use with dynamic capturing of images with the ability to deduct motion artefact and low-cost rendered 3-D images with improved computer vision technology.

Finally, patient-specific computational modelling supports clinical decision-making by providing predictive capabilities accessible from anywhere, representing the future of healthcare. 

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