Artificial Intelligence (AI) is an upcoming and revolutionary technology in many fields
including medicine. AI in interventional radiology (IR) has been shown to be valuable
in diagnosis and predicting outcomes following image-guided therapies. Abajian et
al recently reported that outcomes following transarterial chemoembolization for patients
with hepatocellular carcinoma may be predicted with the use of AI and machine learning
techniques.[1] Their methodology combined clinical data of patients and preprocedural magnetic
resonance imaging. The relative performance of deep-learning neural network for detecting
intracranial aneurysms using three-dimensional TOF-MRA as compared with the radiologists
was recently reported.[2] The role of machine learning in acute stroke has also been highlighted in many studies.[3] In addition, there have been several publications regarding the role of AI in diagnostic
imaging.[4] These reports suggest that the role of AI is not limited to just process automation
but also includes cognitive insight that is so far limited to the human brain.
The potential applications of AI in radiology include the ability to provide rapid
diagnosis especially in the areas where diagnostic radiologists are not routinely
available and improve the confidence in the diagnosis (similar to computer-aided diagnosis
in mammography). The ability to incorporate clinical information, radiomics and patient’s
genetic information may prove AI-aided diagnosis more accurate. Such algorithmic approach
could potentially play a role in triaging patients for IR and subsequent therapy by
assessing the risks and making predictions about therapeutic outcomes. For instance,
if the probability of a breast lesion being malignant is high, the patient may be
considered for an image-guided biopsy. Similarly, in conditions such as stroke, where
“time is money,” AI could not only have a role in rapid diagnosis but facilitate prompt
and effective therapeutic recommendations. Rapid interpretation of the extent of the
penumbra, the potentially salvageable brain, could help the interventional radiologist
decide whether the patient requires mechanical thrombectomy. Similarly, in cases of
pulmonary embolism, AI may help in assessing the “cut off” and “reopened” branches
quickly and assist in therapeutic decision making. Thus, AI in IR would help in optimally
utilizing the manpower and resources. We can also anticipate AI helping IR in detecting
early complications following IR procedures.
The AI techniques are still evolving. The ethical dilemmas in designing the AI algorithms
would need to be discussed and agreed upon by the leaders of the world. More investment
in research and validation of AI algorithms is needed. The other challenges include
its availability, expertise, and economics. While there is a possibility of AI replacing
some of the manpower, it is likely that AI would improve the overall outcomes of patient
care by augmenting the physician performance.
