Using Artificial Intelligence in Radiology

Using Artificial Intelligence 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.

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 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.

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: 

  • 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 by 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 impactful technologies in healthcare. There are numerous areas of adoption, from powering surgical robots to enabling early cancer detection.

In radiology, AI can augment the value practitioners provide their patients, thus improving healthcare outcomes.

AI can streamline workflows and lessen the radiologists’ administrative burden.

Picture of Ferrum Health

Ferrum Health

Simplifying Healthcare AI - Ferrum's AI Hubs enable clinicians to focus on their patients and the care they need, not complicated technology deployment.

Contact Us

CASE STUDY

ARA Health Specialists

Use the button below to download your free case study and learn how our approach to validation has improved the number of clinically significant findings in AI software.

CASE STUDY

Sutter Health

Use the button below to download your free case study and learn how our approach to validation has improved the number of clinically significant findings in AI software.