Artificial Intelligence Is Not Ready For The Intricacies Of Radiology​

Radiology is one of the most essential fields in clinical medicine. Experts in this field are specialists in deciphering and diagnosing disease based on various imaging modalities, ranging from ultrasound, magnetic resonance imaging (MRI), computerized tomography (CT), and x-rays. Studies have shown that the use of radiology in clinical practice has exponentially grown over the years: at the Mayo Clinic, between the years 1999 to 2010, use of CT scans increased by 68%, MRI use increased by 85%, and overall use of imaging modalities for diagnostic purposes increased by 75%, all numbers that have likely continued to rise, and indicate the sheer demand and growth of this robust field.

A unique proposal that has become prominent over the last few years to help alleviate this increased demand is the introduction of artificial intelligence (AI) technology into this field. Simply put, the premise of AI as an addition to the practice of radiology is straightforward, and has been envisioned in two main ways: 1) a system that can be programmed with pre-defined criteria and algorithms by expert radiologists, which can then be applied to new, straightforward clinical situations, or 2) deep learning methods, where the AI system relies on complex machine learning and uses neural-type networks to learn patterns via large volumes of data and previous encounters; this can then be used to interpret even the most complicated and abstract images.

However, while much of the theoretical basis for AI in the practice of radiology is extremely exciting, the reality is that the field has not yet fully embraced it. The most significant issue is that the technology simply isn’t ready, as many of the existing systems have not yet been matured to compute and manage larger data sets or work in more general practice and patient settings, and thus, are not able to perform as promised. Other issues exist on the ethical aspects of AI. Given the sheer volume of data required to both train and perfect these systems, as well as the immense data collection that these systems will engage in once fully mainstream, key stakeholders are raising fair concerns and the call for strict ethical standards to be put into place, simultaneous to the technological development of these systems.

The full Forbes article can be viewed at this link.