AI in Radiology

AI is poised to become a revolutionary force in healthcare. From the early identification of cancer to the treatment of chronic diseases, there are limitless opportunities for leveraging AI in radiology to ensure more equitable and efficient patient care.

Overview of AI in Healthcare

Technology advances in healthcare aren’t a new concept; neither is AI. Algorithms and software mimic human cognition by analyzing, presenting, and interpreting complex healthcare data. What distinguishes AI algorithms from traditional healthcare technologies is gathering and processing data and providing a well-defined output to end-users.

AI supports radiologists by  streamlining   workflows,  discovering genomic markers, identifying errors and supporting quantitative imaging.  Today, there are several scenarios where introducing AI for imaging surpasses human capability. Some of which include improved predictability, diagnostic and treatment accuracy, and increased efficiency.

Despite the growing adoption of radiology AI and the healthcare industry in general, many people still need clarity on the concepts of deep learning and machine learning. Here are the basics:

  • Deep learning is a machine learning technique that is inspired by the way a human brain filters information. It uses computer models to filter the input data through layers to infer information.
  • Machine learning – Machine Learning is a subset of Artificial Intelligence that uses statistical learning algorithms to build systems that have the ability to automatically learn and improve from experiences without being explicitly programmed.
  • Artificial Intelligence – AI enables the machine to make decisions without any human intervention. It is a field in computer science that makes machines seem like they have human intelligence.

Radiology AI: Current Uses and Trends

A typical day in the life of a radiologist involves long hours reviewing a high number of images in addition to performing an array of administrative tasks. It is no surprise that nearly half of all radiologists have experienced burnout at some point. When we imagine the future of radiology, with AI in mind, the goal is that the technology will lessen radiologists’ administrative burden and allow them more time to focus on improving patient care.  Some of the current issues that radiology is facing that AI can help solve for include:

Identifying and Classifying Imaging Markers

Using AI in screening and classification reduces the classification time from 40 minutes to about three minutes. AI helps human radiologists scan as many images as possible. With this more efficient and informed approach, they are able to screen and classify imaging markers more confidently and effectively.

Detecting Hidden Fractures, Bleeding or Clots

AI for imaging can be deployed to detect fractures that hide beneath soft tissue, intracranial hemorrhages, or pulmonary embolisms. In particular, it’s effective in spotting hip fractures which are prevalent in elderly populations. By identifying the type of injury a patient has, doctors can better prepare effective treatment plans.

Discovering Neurological Anomalies

There’s a link between AI and medical diagnosis. AI can help medical practitioners to diagnose common neurodegenerative disorders like ALS, Alzheimer’s, and Parkinson’s by tracking retinal movements. Such analyses can be completed in as little as ten minutes. 

Overcoming Obstacles With AI in Radiology Today

Despite the exciting possibilities surrounding the use of AI in healthcare, several obstacles stand in the way. One of the more complex hurdles is the countless number of AI vendors and algorithms available and the complexity of seamlessly integrating them into the existing infrastructure and clinical workflow. In addition, implementation requires considerable administrative and IT support as well as end-user training.

The ideal solution is an all-inclusive platform such as Ferrum’s AI Hub, a plug-and-play system that lives behind the health system’s firewall, integrates seamlessly, and enables the deployment of multiple AI algorithms. Ferrum’s AI Hub enables clinicians to focus on their patients and the care they need, rather than complicated technology deployment.

Where Is AI in Radiology Headed?

Despite being a relatively new concept in the radiology space, AI is creating a buzz. AI’s rapid adoption in the consumer market has left many wondering how the technology will impact radiology and whether it will replace human physicians. This is an unfounded fear, AI will not replace radiologists. AI cannot address the complex clinical problems and dilemmas that human radiologists manage on a daily basis.

Another emerging prediction for radiology AI and healthcare, in general, is that it will augment a physician’s ability to provide care, especially in developing countries. Stanford University researchers are creating an AI app that will enable medical practitioners to take pictures of X-ray films using their smartphones. The underlying algorithms will scan the film for common ailments such as tuberculosis or a lung nodule. 

The app’s primary benefit is that it leverages AI and doesn’t require physicians to perform complex digital scans to diagnose their patients. In addition, because it works with X-rays, there is no need to invest in advanced digital scanners that are expensive and in short supply in the developing world. Not to mention that most health facilities in these regions do not have radiologists in the first place.

Implications for Healthcare Professionals

There have been fears that radiology AI will replace the need for human physicians. However, these fears are unfounded. AI is meant to complement the work of human radiologists. Generally, human biology is complex, so human physicians need to be present even when AI software is deployed. 

Although radiologists won’t be replaced by the mass adoption of AI in the field, the scope of their work will undoubtedly change. AI is poised to support clinical diagnosis while also taking over administrative tasks such as reporting. To ensure that radiologists are comfortable using AI in their practice, policymakers should consider the following principles when guiding action: 

ai in radiology

Quality Assurance and Oversight: Utilize risk-based approaches to ensure that the use of AI in healthcare aligns with recognized standards of safety, efficacy, and equity. Such frameworks also ensure the appropriate distribution and mitigation of risk and liability. Also, ensure that all algorithms, datasets, and decisions are auditable, clinically validated, and explainable.

Thoughtful Design: Implement AI systems that are informed by real-world workflow, human-centered design and usability principles, and end-user needs. Design, development, and measures of success should leverage collaboration between caregivers, physicians, AI tech developers, and other stakeholders in an effort to have all needs and perspectives reflected.

Education: Support education by advancing the understanding of the capabilities of AI and the intentions of the program, by sharing examples of AI being implemented successfully, presenting emerging opportunities and challenges, etc.

Ethics: Promote existing and emerging ethical norms and standards within the medical community, and require broader adherence by innovators, developers, computer scientists, and those who use AI systems in this new era of healthcare. 

Bias: Address data provenance and bias issues by requiring the identification, disclosure, and mitigation of bias while encouraging access to databases and promoting inclusion and diversity. 

Access and Affordability: Ensure accessibility and affordability, by taking steps to remedy uneven distribution of resources and access.

Current Privacy and Security Frameworks: Acknowledge and address privacy issues, and consent to use data in a particular way. Policy frameworks must be scalable and assure that an individual’s health information is properly protected.

Collaboration and Interoperability: Provide intuitive data access and use through creating a culture of cooperation, trust, and openness. Policymakers, technology developers, users, and the public must work collaboratively.

Key Takeaways

AI is one of the most talked-about technologies in healthcare. There are numerous areas of adoption, from powering surgical robots to enabling early cancer detection. In radiology, AI can increase the value practitioners provide their patients, thus improving healthcare outcomes. It can also streamline workflows and lessen the radiologists’ administrative burden.

Contact Ferrum Health to learn more.


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