# Advancing Medical Decision-Making with AI: A Comprehensive Exploration of the Evolution from Convolutional Neural Networks to Capsule Networks

## TL;DR

This paper reviews CNNs and CapsNets for medical imaging, comparing their strengths and weaknesses to improve AI-based medical decision-making.

## Contribution

A structured comparison of CNNs and CapsNets in medical imaging, highlighting their complementary roles and future hybrid system directions.

## Key findings

- CNNs struggle with spatial variability in medical images, affecting diagnostic reliability.
- CapsNets better capture spatial relationships, improving model generalization and stability.
- Hybrid systems combining both architectures are proposed for more efficient and explainable clinical AI.

## Abstract

In this paper, we propose a literature review regarding two deep learning architectures, namely Convolutional Neural Networks (CNNs) and Capsule Networks (CapsNets), applied to medical images, in order to analyze them to help in medical decision support. CNNs demonstrate their capacity in the medical diagnostic field; however, their reliability decreases when there is slight spatial variability, which can affect diagnosis, especially since the anatomical structure of the human body can differ from one patient to another. In contrast, CapsNets encode not only feature activation but also spatial relationships, hence improving the reliability and stability of model generalization. This paper proposes a structured comparison by reviewing studies published from 2018 to 2025 across major databases, including IEEE Xplore, ScienceDirect, SpringerLink, and MDPI. The applications in the reviewed papers are based on the benchmark datasets BraTS, INbreast, ISIC, and COVIDx. This paper review compares the core architectural principles, performance, and interpretability of both architectures. To conclude the paper, we underline the complementary roles of these two architectures in medical decision-making and propose future directions toward hybrid, explainable, and computationally efficient deep learning systems for real clinical environments, thereby increasing survival rates by helping prevent diseases at an early stage.