Beyond Image Recognition: Discover Use Cases Where Data Labeling Powers AI

What’s the first thing that comes to mind when you think about data labeling? Perhaps tagged images or videos. Like marking an animal in a picture. Or labeling road signs. But did you know that its role goes far beyond that? Data labeling is often the silent hero behind many AI capabilities that today also power critical sectors, including healthcare, manufacturing, and more.  

For example, every time clinical images are tagged with “normal” vs “abnormal”. It empowers AI to assist doctors in screening large patient volumes. This is labelled data at work. Sure, this report must be vet by a human, but you can’t dispute that this process saves time.

This is just one of the many use cases. Different types of data, whether text, audio, or video, require context-specific labeling. Only then can an AI model understand the meaning and provide users with reliable recommendations, reaching its full potential. Let’s examine data labeling in detail and its unique use cases, which continue to shape the accuracy of AI models and, in turn, dictate how we think, live, and work.

What is Data Labeling?

You’re probably already familiar with data labeling. However, if you’re not, or simply as a quick refresher, data labeling means adding meaningful tags to raw data such as words, images, and audio, so that AI systems can understand it. For example, if you present lots of pictures of automobiles/ vehicles, you need to write “car” under the car pictures and “bike” under the bike pictures. This way, ML models understand the meaning, recognize patterns in new, unseen data, and make accurate predictions. Without high-quality labeled data, the AI/ML models would see them as random rectangular shapes and colors without understanding their contextual meaning.

Data Labeling Vs Data Annotation: The Difference

Though often used interchangeably, they aren’t the same. Data labeling usually means assigning a simple tag or category to data. Whereas data annotation goes further. It’s a broader approach that enriches data. It adds layers of information, context, relationships, and details so the AI model can interpret meaning more accurately. 

One way to describe this would be through a word that hasmultiple meanings, such as “bark”. It could refer to a tree’s bark or a dog’s bark. But data annotation doesn’t just mean labeling words but also providing context around them through a sentence. So that AI models can make sense of it. This applies to all types of data, including text, images, audio, and video.

Simply put,

Data labeling- This is apple. (What is it?)

Data annotation- This is a ripe apple sitting on a rectangular tabletop.  (What is it? + Giving the extra details/ context)

Difference	Data Labeling	Data Annotation
Complexity	Usually simple and binary. Helps in identifying key features(yes/no, cat/dog, normal/abnormal).	Provides detailed information about raw data. Can be complex (object boundaries and semantic labels).
Purpose	Training ML models. Datasets are used.	Converting raw data into more machine learnable form such as for computer vision applications.
Example (Text)	Tagging an email as “spam” or “not spam.”	Annotating parts of text with entities, including names, dates, or medical terms.

To know more, read the blog here.

Use Cases of Data Labeling
1. Product Categorization at Online Marketplaces 

Platforms like Amazon and Walmart deal with millions of products. From clothing to smartphones, video games like PS5sand Nintendo, and rare book collections, but they’re all categorized. It makes shopping easier. Users can search and apply filters according to their preferences, making it easier to find the right products more quickly.

2. Spam Detection in Emails

Every day, we receive multiple emails. Some are marketing blasts or phishing attempts, while only a few remain genuinely helpful. To protect users from outright scams, email service providers like Gmail rely on data labeling to identify and block suspicious emails. The process isn’t complicated at all. It’s simply labeling them as either “spam” or “not spam”. For example, subject lines or body copy that contain words like “You’ve won”, “Free”, or “Win a jackpot” are often labeled as “spam”. At the same time, an email about an ATM withdrawal is labeled as “not spam”.

This is a very powerful tool. Over time, AI learns from this historical input and automatically routes suspicious emails or senders into the spam folder. This demonstrates that even something as simple as labeling can have transformative effects on protection from cyber fraud, enhancing safety measures, or improving the user experience.

3. Medical Imaging Classification

In healthcare, data labeling can be used to tag diagnostic images, such as X-rays, MRIs, or CT scans. For example, an X-ray image “showing pneumonia” or “not showing pneumonia”. A simple yes/ no classification or labeling into categories like “normal” or “abnormal”. This helps healthcare professionals in faster decision-making. That said, data annotation is a more effective tool here, as it can highlight the affected lung areas, add notes on their severity, and provide additional information.

Best Practices for High-Impact Data Labeling

I) Clearly Define the Guidelines

Set clear instructions and criteria. This helps prevent ambiguity.

II) Feedback and Guidance

Data labelers or annotators must review and provide feedback on the labeled data at regular intervals. It helps spot any inconsistencies early on and correct them promptly.

III) Prioritize Privacy and Compliance

Especially when labels are tied to personal information such as electronic health records (EHR) and biometrics.

But setting this up on your own can be challenging and time-consuming. Utilizing a data labeling services partner can deliver more value more quickly and cost-effectively.

Conclusion

There is no doubt that data labeling is the cornerstone of successful AI models. That is an accurate description. Seamless categorization. And Reliable training data. It’s all being made possible due to data labeling. It’s a highly relevant tool in today’s times, mainly when more users worldwide rely on Gen AI tools such as ChatGPT, Gemini, or use virtual assistants like Alexa or drive a smart car like Tesla. AI models truly make our lives simpler, improve convenience, and enable more intelligent decision-making every time, and the data labeling function plays a vital role in this ongoing cycle. 

But various industries still rely heavily on manually labeling their data. And poor labeling may lead to poor AI. This is why using a data labeling tool like Labellerr or Labelbox, or partnering with a data labeling services provider, is the way forward.