---
title: "Medical AI"
description: "Medical AI involves the application of artificial intelligence technologies to healthcare, enhancing diagnosis, treatment planning, and patient monitoring by analyzing complex medical data."
date: "2024-07-12"
author:
  name: "Yan Gao"
  position: "Research Scientist"
  website: "https://discuss.flower.ai/u/yan-gao/"
  github: "github.com/yan-gao-GY"
related:
  - text: "Federated Learning"
    link: "/glossary/federated-learning"
  - text: "Server"
    link: "/glossary/server"
  - text: "Client"
    link: "/glossary/client"
---

Medical AI refers to the application of artificial intelligence technologies, particularly machine learning algorithms, to medical and healthcare-related fields. This includes, but is not limited to, tasks such as disease diagnosis, personalized treatment plans, drug development, medical imaging analysis, and healthcare management. The goal of Medical AI is to enhance healthcare services, improve treatment outcomes, reduce costs, and increase efficiency within healthcare systems.

Federated learning (FL) introduces a novel approach to the training of machine learning models across multiple decentralized devices or servers holding local data samples, without exchanging them. This is particularly appropriate in the medical field due to the sensitive nature of medical data and strict privacy requirements. It leverages the strength of diverse datasets without compromising patient confidentiality, making it an increasingly popular choice in Medical AI applications.

#### Medical AI in Flower
Flower, a friendly FL framework, is developing a more versatile and privacy-enhancing solution for Medical AI through the use of FL. Please check out [Flower industry healthcare](flower.ai/industry/healthcare) website for more detailed information.
