Revolutionizing Clinical Radiology: Uncover the Potential of Artificial Intelligence Utility!

Dr. Adam Tabriz

This Article Explores the Potential of AI in Radiology and The Challenges it Faces in Becoming a Primary Tool for Diagnostic Imaging.

Artificial Intelligence (AI) is the hidden disruptor of almost every industry today. Healthcare and radiology, in particular, are familiar with that trend because clinical radiology is the only medical specialty not requiring direct face-to-face patient interaction.

Recently, it has been relatively easy to objectify by establishing a standard for patient anatomical image evaluation, enabling data scientists to design a machine-learning algorithm to pinpoint variations between the human body's healthy and diseased internal structures.

Artificial Intelligence simulates human intelligence by learning the clinically relevant variations and applying those criteria with extreme accuracy and speed to help diagnose many ailments.

The Utility of Artificial Intelligence in Clinical Radiology Has Been Fast

Artificial Intelligence's utility stretches back to 1992 when it was used to detect microcalcifications in mammography or radiological breast images. That breakthrough served as a means of differentiating subtle yet significant image variations that can often not be visible to the human eyes and interpretation. Of course, traditionally, untoward consequences of such circumstances are preventable through additional yet invasive procedures, like biopsy.

The breakthrough in the utility of AI during the 1900s has led to a chain reaction of scientific advances. These include helping radiologists identify subtle pathological changes, prioritizing images that need faster attention, and differentiating between benign and malignant tumors, which may seem similar to the radiologist's eyes—or identifying tumor subtypes without requiring a biopsy.

Artificial Intelligence can render radiological applications sensitive and specific enough for use in screening a variety of diseases or monitoring the healing process without costly procedures.

Artificial Intelligence Benefits Radiologists and Patients Alike but Not Replace Them

Besides many benefits Artificial Intelligence brings to patient care, it also reduces workload, increases efficiency, and prevents burnout for radiologists.

From improving diagnostic capacity and assisting radiologists with high-profile acute disorders, AI is the instrument of choice for the growing number of radiologists. Those are diseases like Pulmonary embolism (Blood Clots in the Lungs), Pneumothorax (Air in the chest cavity), and Intracranial hemorrhage. (Bleeding in the skull)

Artificial Intelligence can help radiologists become productive and assist them in extracting and quantifying radiological image data automatically or semi-automatically. Thus, by ensuring the availability of the appropriate information AI helps radiologists with objective support to diagnose qualitatively and quantitatively repeatedly.

But, Despite all being said and done with, Artificial Intelligence still needs to replace radiologists. Though it revolutionizes the way they apply their skills.

Studies find that Artificial intelligence is not immune to bias. They highly depend on their architects' input and the algorithms they employ. Also, although AI tends to reduce workload at the current standing, some scholars believe the radiologist workload burden can still increase in tandem with AI Utilization in the future. The increasing number of images to be interpreted also means a high turnaround time. That, in turn, makes the utilization cost unpredictable at the current stage.

It is worth emphasizing that Artificial Intelligence is not ready to take on and manage more extensive data sets.

Lately, medicolegal upshots around the utility of Artificial Intelligence are still in their infancy. Significant concerns are still in the air regarding data privacy. Because while AI and Deep Learning require a host of enormous magnitudes of patient data, a lack of proper data security standards, cybersecurity, and privacy infrastructure can have deleterious medicolegal consequences. Therefore, it stands reasonable for healthcare leaders to stop, reevaluate and take cautious and vigilant steps forward at a slower pace.


  1. Long, Marlee. "Artificial Intelligence in Radiology — Aidoc." Aidoc, January 30, 2020.
  2., and MinaMakaryMD. "Artificial Intelligence in Radiology: Current Applications and Future Technologies." HealthManagement, December 21, 2022.
  3. M.D., J.D., Sai Balasubramanian. "Artificial Intelligence Is Not Ready For The Intricacies Of Radiology." Forbes, February 3, 2020.
  4. TABRIZ, Dr. ADAM. "Artificial Intelligence, Deep Learning, and Medicine." Medium, September 1, 2021.
  5. TABRIZ, Dr. ADAM. "Artificial Intelligence, Machine Learning, Big Data and Health Information: What You Need to Know!" Medium, November 8, 2022.
  6. TABRIZ, Dr. ADAM. "Physicians Are Working Like Robots for Robots." Medium, July 20, 2022.
  7. TABRIZ, Dr. ADAM. "The Paradox of Empathetic Transference in Medicine: Empathic Technology vs. Algorithmic Sympathy." Medium, July 5, 2021.
  8. TABRIZ, Dr. ADAM. "Healthcare Technology Rush: The Major Factor behind the Disconnect between Physicians and Their…." Medium, July 11, 2021.
  9. TABRIZ, Dr. ADAM. "Deep Learning Gamification and Artificial Intelligence as a Modern Learning Tool, or an Agent Of…." Medium, July 25, 2021.
  10. TABRIZ, Dr. ADAM. "Medicolegal Perils of Artificial Intelligence and Deep Learning." Medium, July 5, 2022.

Initially published by Technology Hits

Comments / 0

Published by

Politics | Health | Healthcare | Humanity

San Francisco, CA

More from Dr. Adam Tabriz

Comments / 0