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AI Transforming Radiology: An Overview of the Latest Developments

Explore the transformative role of artificial intelligence (AI) in healthcare, particularly in the realm of radiology. This article delves into the latest advancements in AI radiology software, highlighting its potential benefits, challenges, and future implications.

AI transforming Radiology: Introduction

Artificial intelligence (AI) is the set of systems or algorithms created to imitate (and in some cases, improve) human intelligence to perform certain tasks, with the characteristic of “learning” on the go and perfecting its processes as more information is gathered.

Although the subject of artificial intelligence has recently been a hot topic, it is not really something new. In some fields, this science has been developing for some years and with impressive results. Such is the case of artificial intelligence in healthcare and its application in imaging techniques.

In this blog post, we will provide an overview of the latest AI developments in radiology and discuss the potential benefits and challenges of AI transforming radiology.

Artificial Intelligence in Healthcare

AI in healthcare involves the development of new informatics tools for the discovery of drugs and molecular targets, using computer engineering and artificial intelligence techniques.

The most popular AI applications used in the healthcare sector that represent the largest proportion, in terms of use and expectations; are currently the following:

Despite the context surrounding the development of AI, the dizzying growth it has achieved in the last 5 years is undeniable. AI applied to healthcare could save the medical industry $150 billion. Moreover, the market has a lot of growth potential. In 2014 it was $600 million, and by 2020 it is estimated to be $6.6 billion, an 11-fold increase.

AI transforming radiology

Medical image interpretation is one of the main tasks performed by the radiologist. Making computers capable of performing such cognitive tasks has been both a challenge and one of the main goals in the field of computer vision for years. Thanks to technological advances, autonomous healthcare is now a reality.

Within a Radio diagnostic service, AI systems can be applied in multiple areas, such as in tasks related to:

  • Image interpretation
  • Image post-processing (reconstructions, image quality enhancement, etc.)
  • Appointment of patients
  • Selection of the best imaging protocol and radiation dose
  • Patient positioning on the equipment

How important is radiology in medical decision-making?

It is estimated that approximately 10% of outpatient consultations require a radiological examination, as well as 50% of patients seen in emergency departments, and 70% of hospitalized patients.

On the other hand, the data generated from the images are complex, often ambiguous and implicit, and it is a challenge to process all this information in the short time allotted for each patient. However, as images become more and more quantitative, they are transformed into information that is easier to analyze by machines.

Thus, the possibility of collaboration between humans and computers arises. The future of imaging belongs to quantification, and it is artificial intelligence that will make it possible to reduce the information to only what is relevant, which in turn will result in multiple benefits:

  • Optimized time per patient
  • Increases diagnostic accuracy
  • Decreases the risk of iatrogenic or malpractice
  • Healthcare costs are optimized
  • Reduce practitioners overload

According to a research article conducted by experts from the University of Norway, AI radiology has a high capacity:

“Artificial intelligence (AI) for radiology has the potential to handle an ever-increasing volume of imaging exams.”

Will radiologists be replaced by artificial intelligence?

Artificial intelligence will not replace radiologists, but what is clear is that healthcare professionals who use artificial intelligence will replace those who do not. However, it is necessary to keep in mind that while these advanced applications are facilitating diagnostic imaging, more advanced models are being developed in parallel in increasingly competent areas of radiology.

Watson, an AI computer system developed by IBM, was recently able to demonstrate thrombi in pulmonary arteries on a computed tomography (CT) scan during a medical congress a few years ago.

Latest advances in AI in radiology

AI has revolutionized radiology in recent years thanks to the continuous evolution of deep learning algorithms and neural networks. New advances have enabled AI to identify and classify multiple pathologies in a single image with much greater accuracy than was possible with conventional methods.

In addition, precision medicine is one of the most promising emerging applications of AI in radiology. AI can analyze large amounts of patient data to identify patterns and predict possible treatment outcomes. This allows physicians to personalize treatment for each patient more effectively and accurately. Further, Picture Archivation and Commuication Systems (PACS) help to increase accuracy and communication.

An article in Magnetic Resonance Imaging portends a boom in the capabilities of AI implementation in radiology:

“It is plausible that someday all images will be prescreened and analyzed by a computer before human interaction. The extent to which this will obviate the need for specific professional skills remains to be determined.”

Predicting response to treatment

One of the main utilities of the implementation of artificial intelligence in radiology is to be able to predict and analyze the patient’s response to chemotherapy or radiotherapy treatment to assess the patient’s diagnostic and survival possibilities.

Tumor staging

They can differentiate between diseases at an early stage or an advanced stage, thus allowing an approximation to be made to tumor staging, which, although not yet at the levels of pathological anatomy, it is likely that with strong development it could complement or even replace it as Gold standard in the objectification of the stage of tumor entities.

Tissue identification

A representative example of this is the study of pulmonary nodules on CT. Pulmonary nodules at the visual level have always been a problem to make a differential diagnosis simply with the image, and artificial intelligence software has been developed capable of discriminating nodules that belong to:

  • Bronchogenic carcinomas
  • Organized pneumonia
  • Tuberculomas
  • Hamartomas

Where are we now and where are we going?

An opinion article recently published by scientists from Harvard Medical School points out that at present AI imaging software is capable of facilitating the work of radiologists so that the interpretation work is done jointly.

However, as can be seen in the following diagram obtained from the same article, it is foreseen that in the future, artificial intelligence will take over the control of diagnostic imaging, without the need for human interpretation.

Nat Rev Cancer, 2018

In conclusion, the adoption of AI in radiology is leading to a significant transformation in healthcare. It enhances diagnostic accuracy, optimizes patient care, and paves the way for promising future advancements. With platforms like TestDynamics’ Satori, these AI algorithms are becoming increasingly accessible, leading to an unprecedented revolution in the field. As we move forward, the combination of human intelligence and AI transforming radiology will be crucial in enhancing patient outcomes and redefining the healthcare landscape.

Discover the most Powerful Medical Imaging AI Platform

If you’re prepared to discover how the Satori platform, can revolutionize your healthcare delivery, we invite you to reach out to us. Let’s collaborate to harness the transformative power of these advanced technologies, propelling your medical practice to unprecedented levels of diagnostic accuracy, efficiency, and patient care.

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