How Artificial Intelligence in Healthcare is Going to Play a Key Role for Medical Imaging?

Researchers are always in the quest of implementing advanced technology in improving the quality of human life. The Healthcare sector is one of the prominent filed where new technologies are used at a wider scale. Artificial intelligence (AI) has emerged as one of the latest technologies in the tech industry providing promising opportunities in the healthcare sector.

AI in healthcare is effectively used for medical imaging in radiology and other sub-fields making the decision-making process faster and quicker. And further, artificial intelligence is playing a bigger role in medical imaging and image recognition service. So let’s find out how AI can hold a key to the future of medical imaging and analysis in the healthcare industry.

What is Medical Imaging and How AI is used in Medical Imaging?

Medical imaging is a kind of technique used to create a visual representation of the interior of the human body for clinical diagnosis and medical intervention. Medical imaging establishes a kind of database of normal anatomy and physiology to identify the abnormalities in the body. And each typed of AI-backed technology in medical imaging gives different information about the area of the body being studied or treated, diseases, or how far the medical treatment is effective in curing the illnesses.

AI technology is used in image annotation to recognize the patterns equipped with thousands of predictive algorithms to automatically detect such patterns of the most standard diseases and indicate the type of disorder in the body with maximum accuracy. Google has recently developed AI-based software that can predict the patient’s death risk in hospitals with 95% accuracy allowing doctors to take favorable decisions timely.


How will AI bring a revolution in Medical Imaging?

Despite various challenges in the medical imaging field, AI is bringing a positive transformation into the healthcare sector. Here we will discuss how AI will revolutionize medical imaging and make the medical care and treatment process more effective and helpful.

Read More: Top Five Best Usages of Artificial Intelligence in Healthcare Medical Imaging.

Image Annotation with Automated Interpretation

AI-enabled devices can annotate pictures of medical imaging with the disorder in the body. And based on its image recognition capabilities it will also automatically prepare the report after complete analysis and interpretation of results. Currently, such tasks are usually performed by humans and it could be an incredibly challenging task for machines to predict accurate results. However, with more improvements in AI-enabled diagnostic systems, medical imaging with machines will become more precise and accurate making it easier for the radiologist to take decisions and provide the best treatment to patients at an affordable cost.

Radiologist can take better Decisions

Though, humans can take such decisions better than machines but sometimes due to emotional or sentimental factors their decisions can be affected at a certain level. Whereas AI-backed medical image annotation will help to minimize the errors and discrepancies, especially at the time of reporting and analysis. And using the correct metadata and natural language processing with the right healthcare training data could be another method of reducing errors and malpractice costs by taking quick and better decisions by the radiologist also saving the time and cost of the entire hospital.

Challenges and Risk Factors

Medical imaging or image annotation with AI-backed systems is based on a source of data that helps to diagnose the diseases learned from training data feed into machine learning with the right algorithm. Machine learning improves what imaging provides to enhance the role of imaging in diagnosis much beyond the usual disease staging. However, there are few challenges and risk factors that affect the medical imaging process.

Data privacy, size of annotated data, and quality of annotation tools are few things that need to be considered at the time of image annotation outsourcing allotted to companies providing image annotation or medical imaging data services. The quality of data and algorithms also affects the results given by these AI-developed machines. However, with more improvements in technology and better use of the machine learning process, more revolutionary changes will come making this technology more vital for the healthcare industry.

In the nutshell, with the rise in more proactive applications of artificial intelligence in healthcare, medical imaging is also set to drastically change how patients and doctors interact with each other during the treatment process. However, such impacts will be completely driven by technology and economics and would be more gradual rather than revolutionary. And it would also a paradigm shift for the radiologist to work at the same time with AI-backed machines to enhance patient care and treatment procedure.