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Artificial intelligence (AI) has piqued the interest and imagination of businesses across the board, and healthcare is no different. The field of radiology has already benefited from AI advancements. According to a recent ACR Data Science Institute survey, nearly a third of radiologists are currently using AI in their clinics. However, such practices only use an average of little more than one algorithm, implying a limited number of tools and great possibility for expansion once users locate the ideal AI applications for their purposes.

Although artificial intelligence is still developing, it has been proven effective in various industries and in achieving several key health goals. Here are four ways artificial intelligence is changing radiology as we know it.

1. Identifying problems and improving workflows

A radiologist uses a mix of knowledge, experience, and a good eye to evaluate medical images, often looking at a variety of different aspects in order to develop conclusions. Although artificial intelligence is still a long way from replicating this capacity, it has shown that it can spot some possible abnormalities in medical imaging. MammoScreen and CMTriage from CureMetrix, for instance, can detect the chance of malignancy in mammograms.

CureMetrix put this capability to the test at the recent Radiological Society of North America meeting, proving that radiologists using AI performed 40 percent faster on reads and had a 25% higher accuracy rate.

2. Triaging emergencies

Throughout the Covid crisis, several locations became overburdened with patients and required assistance with triage – this was especially true in the early months of the pandemic when healthcare staff was still learning to diagnose and manage the disease.

Imaging, particularly chest x-rays, proved to be one effective approach for spotting worrisome symptoms in hospitals and clinics swamped by Covid symptoms.

This requirement prompted Qure.ai, a healthcare start-up, to repurpose their AI-powered chest x-ray tool to scan for signs of Covid, allowing overworked frontline physicians throughout the world to better handle cases. This is a terrific example of how artificial intelligence (AI) may be used to assist healthcare staff in emergency situations, as well as how a clever corporation can quickly create its own technology to match those needs.

3. WEARABLES AND PERSONAL DEVICES FOR HEALTH MONITORING

Almost every customer now has access to gadgets with sensors that can collect useful health information. A growing amount of health-related data is created on the road, from cell phones with step trackers to wearables that can detect a heartbeat around the clock. Collecting and analyzing this information, as well as complementing it with the information provided by patients via apps and other home monitoring devices, can provide a unique perspective on individual and population health.

Artificial intelligence will play a key role in deriving relevant insights from this massive and diverse data set. We’ve been fairly promiscuous with our digital data as a society. However, as more information becomes available, people will become more cautious about who they share what information with.

Patients, on the other hand, are more likely to trust their doctors than they are to trust a major corporation, which may assist in alleviating any apprehensions about donating data to large-scale research projects. Because our treatment is episodic and the data we collect is coarse, there’s a significant probability that wearable data will have a major impact. Collecting granular data in a continual manner increases the possibility that the data will assist in providing improved patient care.

4. Provide healthcare services in underserved communities

The persistent problem of hospital closures in rural parts of the United States, as well as a shortage of specialists both here and overseas, might result in a backlog in medical imaging review. So, even if it is possible to supply technology such as CT scanners and ultrasound machines to underserved areas, there is no guarantee that a competent person will be available to install them. In these resource-strapped locations, AI is offering much-needed assistance. AI tools that can scan through photos and indicate ones that appear to have anything wrong with them can help prioritize images that need a radiologist’s attention more urgently. The app can also be configured to automatically send marked photos to another radiologist for examination

Read Official Blog Post – How Will Artificial Intelligence Impact Healthcare Sectors?

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