# Evaluation of deep learning tools in medical diagnosis and treatment of cancer: research analysis of clinical and randomized clinical trials

## TL;DR

This paper evaluates deep learning tools in cancer diagnosis and treatment through clinical trials, highlighting their potential and challenges in precision medicine.

## Contribution

A systematic review of deep learning algorithms in cancer care, stratified by cancer type and trial design.

## Key findings

- DL models show promise in analyzing complex data for accurate cancer predictions.
- Performance varies across cancer types, with some showing stronger algorithmic outcomes.
- Barriers to large-scale implementation include data quality and integration challenges.

## Abstract

Artificial Intelligence and machine learning tools have brought a revolution in the healthcare sector. This has allowed healthcare providers, patients, and public to be at pole position -amidst the key consideration and barriers-to attain precision and personalized medicine. Deep Learning (DL) is a branch of machine learning and AI that has become transformative for healthcare and biomedicine, providing the ability to analyze large, complicated data, capture abstract patterns, and present fast and accurate predictions. DL models are based on complex neural networks that emulate biological neural networks. In this paper, our goal is to evaluate DL algorithms in clinical trials stratified per cancer type and present future perspectives on the most promising DL approaches. We systematically reviewed articles on deep learning in cancer diagnostics in studies published in the Pubmed database. The searched literature included two types of articles, clinical trials, and randomized controlled trials. The deep learning algorithms used in the targeted literature are reviewed, and then we evaluated the performance of the algorithms used in disease prediction and prognosis. We aim to highlight the promising DL approaches reported per cancer type. Finally, we present current limitations and potential recommendations in large-scale implementation of deep learning and AI in cancer care.