Future Artificial Intelligence tools and perspectives in medicine

TL;DR

This paper reviews recent advances in AI-driven radiomic tools for medical diagnosis, emphasizing deep learning techniques and their potential to improve non-invasive cancer analysis.

Contribution

It provides a comprehensive overview of current radiomic pipelines and discusses how deep learning enhances predictive models in medical imaging.

Findings

AI aids in non-invasive cancer diagnosis

Deep radiomic analysis improves model accuracy

Further validation needed across cancer types

Abstract

Purpose of review: Artificial intelligence (AI) has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical variables, etc.) and used the statistical and machine learning methods to measure the model performance. In this review, we summarized and discussed the most recent radiomic pipeline used for clinical analysis. Recent findings:Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. Most AI tools are based on imaging features, known as radiomic analysis that can be refined into predictive models in non-invasively acquired imaging data. This review explores the progress of AI-based radiomic tools for clinical applications with a brief description of necessary technical steps. Explaining new radiomic approaches based on deep learning techniques will explain how the new radiomic models (deep radiomic analysis) can benefit from deep convolutional neural networks and be applied on limited data sets. Summary: To consider the radiomic algorithms, further investigations are recommended to involve deep learning in radiomic models with additional validation steps on various cancer types.