---
title: Ideas for customization
weight: 50
aliases: /medical-diagnosis-amx/ideas-for-customization/
---

include::modules/comm-attributes.adoc[]

:toc:
:imagesdir: /images
:_content-type: ASSEMBLY

//Module to be included
//:_content-type: CONCEPT
//:imagesdir: ../../images

[id="about-customizing-pattern-med"]
= About customizing the pattern {med-pattern}

One of the major goals of the {solution-name-upstream} development process is to create modular and customizable demos. The {med-pattern} is just an example of how AI/ML workloads built for object detection and classification can be run on OpenShift clusters. Consider your workloads for a moment - how would your workload best consume the pattern framework? Do your consumers require on-demand or near real-time responses when using your application? Is your application processing images or data that is protected by either Government Privacy Laws or HIPAA?
The {med-pattern} can answer the call to either of these requirements by using  {serverless-short} and {ocp-data-short}.

[id="understanding-different-ways-to-use-med-pattern"]
== Understanding different ways to use the {med-pattern}

. The {med-pattern} is scanning X-Ray images to determine the probability that a patient might or might not have Pneumonia. Continuing with the medical path, the pattern could be used for other early detection scenarios that use object detection and classification. For example, the pattern could be used to scan C/T images for anomalies in the body such as Sepsis, Cancer, or even benign tumors. Additionally, the pattern could be used for detecting blood clots, some heart disease, and bowel disorders like Crohn's disease.
. The Transportation Security Agency (TSA) could use the {med-pattern} in a way that enhances their existing scanning capabilities to detect with a higher probability restricted items carried on a person or hidden away in a piece of luggage. With Machine Learning Operations (MLOps), the model is constantly training and learning to better detect those items that are dangerous but which are not necessarily metallic, such as a firearm or a knife. The model is also training to dismiss those items that are authorized; ultimately saving passengers from being stopped and searched at security checkpoints.
. Militaries could use images collected from drones, satellites, or other platforms to identify objects and determine with probability what that object is. For example, the model could be trained to determine a type of ship, potentially its country of origin, and other such identifying characteristics.
. Manufacturing companies could use the pattern to inspect finished products as they roll off a production line. An image of the item, including using different types of light, could be analyzed to help expose defects before packaging and distributing. The item could be routed to a defect area.

These are just a few ideas to help you understand how you could use the {med-pattern} as a framework for your application.

//We have relevant links on the patterns page
//AI: Why does this point to AEG though? https://github.com/hybrid-cloud-patterns/ansible-edge-gitops/issues[Report Bugs]
