# Review of deep learning in healthcare

arXiv: https://arxiv.org/abs/2310.00727 · HTML: https://mlsanity.com/paper/2310.00727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)](https://mlsanity.com/paper/2103.08182/) (2021)
- [Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes](https://mlsanity.com/paper/2103.08186/) (2021)
- [Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases](https://mlsanity.com/paper/2305.13333/) (2023)
- [Using VGG16 Algorithms for classification of lung cancer in CT scans Image](https://mlsanity.com/paper/2305.18367/) (2023)
- [Feature selection for medical diagnosis: Evaluation for using a hybrid Stacked-Genetic approach in the diagnosis of heart disease](https://mlsanity.com/paper/2103.08175/) (2021)
- [Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges](https://mlsanity.com/paper/2308.01319/) (2023)
- [A comprehensive review of deep learning in lung cancer](https://mlsanity.com/paper/2308.02528/) (2023)

## Abstract (arXiv)

Given the growing complexity of healthcare data over the last several years, using machine learning techniques like Deep Neural Network (DNN) models has gained increased appeal. In order to extract hidden patterns and other valuable information from the huge quantity of health data, which traditional analytics are unable to do in a reasonable length of time, machine learning (ML) techniques are used. Deep Learning (DL) algorithms in particular have been shown as potential approaches to pattern identification in healthcare systems. This thought has led to the contribution of this research, which examines deep learning methods used in healthcare systems via an examination of cutting-edge network designs, applications, and market trends. To connect deep learning methodologies and human healthcare interpretability, the initial objective is to provide in-depth insight into the deployment of deep learning models in healthcare solutions. And last, to outline the current unresolved issues and potential directions.