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Current Oncology Reports

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01-06-2021 | Artificial Intelligence | Interventional Oncology (DC Madoff, Section Editor)

Role of Machine Learning and Artificial Intelligence in Interventional Oncology

Authors: Brian D’Amore, Sara Smolinski-Zhao, Dania Daye, Raul N. Uppot

Published in: Current Oncology Reports | Issue 6/2021

Abstract

Purpose of review

The purpose of this review is to highlight the current role of machine learning and artificial intelligence and in the field of interventional oncology.

Recent findings

With advancements in technology, there is a significant amount of research regarding the application of artificial intelligence and machine learning in medicine. Interventional oncology is a field that can benefit greatly from this research through enhanced image analysis and intraprocedural guidance. These software developments can increase detection of cancers through routine screening and improve diagnostic accuracy in classifying tumors. They may also aid in selecting the most effective treatment for the patient by predicting outcomes based on a combination of both clinical and radiologic factors. Furthermore, machine learning and artificial intelligence can advance intraprocedural guidance for the interventional oncologist through more accurate needle tracking and image fusion technology. This minimizes damage to nearby healthy tissue and maximizes treatment of the tumor. While there are several exciting developments, this review also discusses limitations before incorporating machine learning and artificial intelligence in the field of interventional oncology. These include data capture and processing, lack of transparency among developers, validating models, integrating workflow, and ethical challenged.

Summary

In summary, machine learning and artificial intelligence have the potential to positively impact interventional oncologists and how they provide cancer care treatments.

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Title: Role of Machine Learning and Artificial Intelligence in Interventional Oncology
Authors: Brian D’Amore
Sara Smolinski-Zhao
Dania Daye
Raul N. Uppot
Publication date: 01-06-2021
Publisher: Springer US
Keywords: Artificial Intelligence
Interventional Radiology
Published in: Current Oncology Reports / Issue 6/2021
Print ISSN: 1523-3790
Electronic ISSN: 1534-6269
DOI: https://doi.org/10.1007/s11912-021-01054-6

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Abstract

Purpose of review

Recent findings

Summary

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