AI in Healthcare: Radiology

obody can deny the versatility of AI; name the field, and it is there. The roots of AI are expanding and getting firm across every field it passes. With the rise of data and complexity, another area with potentially far-reaching implications is AI in healthcare and, more precisely, in RADIOLOGY. Through the article, you will explore the need for AI in healthcare and its impact on the treatment for various diseases, such as  TB and strokes, followed by reasons for why AI is the solution to a number of problems in radiological fields. The article will elaborate on information around how AI can advance radiological practices and  the opportunities and challenges on its way.

AI is a group of technologies, and most of them have immediate applicability in the healthcare field with broad support to varying tasks and specific processes. The most significant technology to healthcare is Machine learning – neural networks and deep learning.

Machine learning, the most common form of AI, has many versions and is a widely used core-technology used in  many approaches. Deep Learning or neural network models are complex forms in machine learning. Many of the thousands of hidden features in such models are yet to be unveiled by today's GPUs and cloud architectures' faster processing ability. The recognition of cancerous lesions is a widespread application of deep learning in radiology images. It is increasingly being applied to radionics or detecting clinically relevant imaging data features to perceive beyond human eye capability. Their combination appears to promise greater accuracy in human diagnosis than the previous generations of automated tools for image analysis known as computer-aided detection or CAD. 

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in tasks related to image-recognition. It excels at automatically recognizing complex imaging data patterns, and providing quantitative, rather than qualitative, assessments of radiographic attributes. It offers a multitude of possibilities for transforming how radiological services are delivered.


But before that, you might have been thinking, "What is Radiology?" Am I right? Yes! So here we go.


Radiology is a medical specialty used to interpret the images of body organs when attempting to  diagnose abnormalities in the body . Doctors rely on preliminary diagnosis for most diseases using radiology imaging, such as MRIs, X-rays, and CT Scans to create treatment regimens for their patients.

It depends on machines; thus, it has experienced nearly constant evolution compared to other medical fortes. Simultaneously, as it is well-known, information technology (IT) has progressed immensely during the new millennium. AI, a complex form of IT, has been rapidly developing as a highly innovative area in radiological science in recent years.

How is AI going to bring a change in healthcare? 

Since the advent of AI, its impact on the healthcare sector has been the town's talk, and why not. Machines helping in a crucial medical analysis are indeed something unusual to imagine. Its emergence arouses curiosity regarding how it will impact the way people use healthcare services around the world, specifically the interpretation of radiology images. 

You may know that the Indian government has set a target of eradicating TB from the country by 2025, and AI will be the primary tool for realizing this goal. In rural India, access to public health centers is kilometers away for most TB patients. They typically get a Chest X-ray scan after meeting the physician: That’s after walking all the way. After completing the checks, there  is often no radiologist available to read the Chest X-ray on the spot, and the patient will have to re-visit for a diagnosis and medication. A substantial change can be experienced by using AI to automatically read Chest X-rays in real-time, and give immediate feedback to the physicians for microbiological confirmation of TB, allowing them to immediately start their patients' treatment regimens. Strokes, a leading cause of death worldwide, can also be diagnosed and treated. There are methods of treatment that exist , but  delay could lead to significant debilitation or even death. Therefore, AI can assist here by automatically identifying disease and marking out the diseased areas so that the general physician on duty can make the right treatment decision.

Why is AI a solution to Radiology?

The accuracy of diagnosis can be enhanced using AI to ease providers' burden and overcome challenges to limited remuneration.

  • Improving the Accuracy of Imaging Reads - A flawed or wrong positive/negative image read can significantly impact patients. Repeated and irrelevant tests can be emotionally, physically, and mentally draining for the patients as they can be costly, resulting in the mounting  of their medical bills. There has been a decline in the false read rates with the involvement of AI, as compared to the analysis of radiologists. Image readings’ enhanced accuracy has helped patients requiring urgent treatment to have an early diagnosis and immediate treatment.
  • Creating Time for Providers - Following the recent studies, the algorithms are capable of reading the images faster than an average radiologist. Incorporating AI into regular clinical workflow would assist in reading more images in less time. Therefore, resulting benefits can be shown through reduced burnout and more time focussing on patient care. Thus, in the form of a helping hand, AI would allow the doctors to engage in the tasks that machines can not do, like counselling patients, creating a care plan, helping patients understand their diagnosis and treatment options, and coordinating care with other providers.
  • Solving the Reimbursement Dilemma - The declining rate of reimbursement for individual reads has been one of the reasons for concern for the providers, as due to this, they need to read more images to maintain the same rate. With the relocation of the healthcare industry from fee-for-service to outcomes-based reimbursement models and assistance of AI, providers can now focus on the quality and value-based care rather than the number of services provided


Opportunities 'AI' creates in Radiology.

While the opportunities are limitless, it takes a lot of work to make this happen.

  • Quantitative Imaging - It is about precision and proof-based medicine; Results are required to be, even with reduced variability across devices, patients, and time. Equitable and quantitative metrics will allow "personalization" of disease by imaging for individuals or a population.
  • Improved Communication Technology - Improvement in networking and communication will improve the data available anywhere and anytime by offering active sharing of databases such as Picture Archiving and Communication Systems (PACS), Radiological Information Systems (RIS), and integrating the Healthcare Enterprise (IHE). Decision support technology will improve utilization management by enabling better explanation, suitability, and economically responsible 'value-based radiology' in the spirit of 'accountable care.' Also, Telemedicine offers better use of human resources by recruiting and employing radiologists independent of geographical location and time zone. Workflow management will have enhanced analytic tools to help in clinical prioritization and the pre-detection of abnormal cases, with swift reporting of top-priority findings.
  • Interventional Radiology - It is an excellent opportunity as it retains two major disruptive elements, i.e., minimizing invasiveness and lowering costs. Newer techniques that will reduce radiation to both patient and physician and smarter semi-automated guidance software will shorten the procedure time and improve safety.


What are the current challenges for AI in radiology?

The sky seems to be the limit when it comes to applying AI in radiology. But with every change, there come the challenges, along with the opportunities. So, let's have a look over challenges that need to be fixed before the full adaptation of AI in radiology workflow.

  • A Sufficient Amount of Quality Labeled Data - In the medical field, high-quality datasets are not straightforward, whereas other general databases are powerful because of the massive amount of accurately labeled images. It can be concluded that the volume available is still several orders of magnitude behind as for the typical medical imaging dataset of 1000 images has to be compared to a non-medical database with up to 100,000,000 pictures. It can be solved using Augmentation.
  • Dealing with a 3D Reality - Usually, CT- and MRI- images are 3D, but currently, the most prominent deep learning models are being trained on simple 2D. Conventional X-ray images may be 2D; however, most of the current deep learning algorithms are not adjusted to the larger groups of images being used due to their projected character. Experience needs to be gained by applying deep learning to these types of images. 
  • Non-Standardized Image Acquisition - Varying scanner types and different accession settings, the non-standardized addition of medical images creates a problematic situation for artificial intelligence algorithms' training. The more the variation in the data, the significantly bigger the data sets need to be to guarantee that the deep learning network yields in a robust algorithm. To overcome this problem, apply Transfer Learning, a pre-processing technique to overcome scanner and acquisition specifics.
  • A Smooth User-Experience - According to the radiologists' most common feedback, the current radiology software is not satisfactory, as it requires too many clicks, long waiting times, and the inability to quit once the process starts. A need for a more user-centric and friendly application experience is essential to ensure radiologists keep using it in the clinic. Though it is not as easy as it sounds, many different efforts like creating algorithms, verifying its working through the product development process, rectifying the errors, etc. go into building radiology software.

Though AI in healthcare sounds a bit critical, we can't deny the assistance and benefits to the radiologists. The list of opportunities it creates and the advancement it brings should be valued. AI in Radiology is far ahead when compared to other healthcare areas because radiology data is already digitized. So radiologists have to be proactive to survive in this rapidly changing world of radiology. AI will increase efficiency, the clinical effectiveness of the service, and speed up the referral process. The initial phases are always challenging, like in the healthcare sector, AI struggles with the user's trust. The problem lies in each area of its application, but the real win lies over consistent improvements till it turns into an opportunity.

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