Training Data to Employ AI in Healthcare

As artificial intelligence (AI) becomes an increasingly important tool in health care, it offers unprecedented opportunities for improving patient outcomes, reducing costs, and impacting population health. There are many examples, including automation, delivering a simple synthesis of complex health information to patients, families, and caregivers, and providing recommendations and visualizations for shared decision-making among patients, family members, and health professionals.

Need for AI Technology Adoption in Healthcare

As healthcare organizations realize the benefits of technology adoption as patient numbers rise and quality and affordable treatment become more important, they are adopting artificial intelligence. It is still early in the adoption of artificial intelligence in healthcare. The reason behind this is that many emerging economies lack the means of digitizing patient records. 

The increased number of diseases and the need for better understanding and diagnosis with the help of deep learning are some of the reasons for AI adoption in healthcare. In many healthcare organizations, AI has enabled them to treat more patients at lower costs and with improved efficiency. 

Need for Building Database for Healthcare AI

To effectively utilize AI in the healthcare industry, there is a need to build a database with all the historical data of patients, required to identify the patterns and accordingly use AI for diagnosis and therapy. If customized accurately, AI would not only be ‘intelligent’ but also highly user-friendly. 

It is also necessary to maintain records physically in certain countries in South Asia and Africa. However, the adoption of AI is relatively less challenging in developed countries such as the US and the UK, where the majority of healthcare data have already been digitized, and the technology has flourished. 

Significance of Data for AI Utilization in Healthcare 

In healthcare, data can be related to health conditions, reproductive outcomes, quality of life, and many similar life events determining an individual or an entire population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioural information pertinent to health and wellness.

After getting to know the most important terms, definitions, and methods used in the field of artificial intelligence, we need to be familiarized with the current situation of data and data analytics in healthcare in order to be able to find out where artificial intelligence can come into play in healthcare; why and how we could make use of machine learning and smart algorithms – as the most important ‘base material’ for smart algorithms is data.

AI technologies that can Transform Healthcare

AI is based on technologies such as Natural Language Processing, Deep Learning, and Context-Aware Processing. The healthcare industry is undergoing a transformation as a result of these technologies. 

  1. Natural Language Processing (NLP) 

In today’s world of complex data, AI plays a significant role in simplifying insights, and NLP makes the process more accessible to more people. The goal of Natural Language Processing (NLP) is to mimic the responses of humans when answering queries and holding conversations by utilizing algorithms. Using NLP healthcare providers can summarize narrative text by highlighting key concepts or phrases, such as those found in long journal articles or clinical notes. 

Data elements in EHRs, which are represented as unstructured text, can be transformed into structured, meaningful data by using Natural Language Processing. Using NLP, Intermountain Healthcare, for example, has identified the causes of illness in patients by mining their medical records in order to identify cases like strokes, cancer, heart failure, and venous thromboembolisms (blood clots in the leg’s deep veins).

  1. Deep Learning 

By implementing AI in healthcare, diseases can be diagnosed and treated more efficiently. Medical data and images can be analyzed using deep learning, a component of AI, to enhance the ability of doctors to treat diseases. Visually challenged individuals can benefit from deep learning by narrating the text, identifying the facial expressions of nearby people, studying the surroundings, and describing the environment using computer vision, text-to-speech, and computer vision. 

Advanced neural network algorithms, GPUs that can process large amounts of data, and access to enormous amounts of internet data are all driving the deep learning revolution. A cognitive computing & deep learning approach is being used to train IBM Watson, for example, to assist doctors with medical diagnosis.

  1. Context-Aware Processing 

As examples of AI applications that can be used in healthcare, Apple’s Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana can provide assistance based on the instructions provided by the programmer. Using AI chatbots in healthcare can eliminate the burden on medical professionals and reduce the time and cost of coordinating care and detecting issues or diagnosable health issues. 

Bots could be considered the next step in the transformation of health assistants. HealthTap, for instance, or Your. Md are Bots that use AI to diagnose the most common symptoms. Nevertheless, chatbots play an important role in directing patients to the right doctor for diagnosis and treatment. Doctors with experience would be able to rely on them to supplement their duties. 

  1. Intelligent Robotics 

There is also the possibility of using AI with robotics. Healthcare facilities can be revolutionized with physical robots that can help people stay healthy and reduce the number of hospitalizations. In addition to AI, advanced humanoid designs are enabling robots to converse with and socially interact with elderly patients. With robots, surgery can be performed with more precision and smaller incisions since their flexibility and reach are greater. 

Certain robots can serve as social partners to treat mental health issues or alleviate loneliness. Blue Frog Robotics (developers of BUDDY), National Institute of Advanced Industrial Science and Technology, and AIST (developers of PARO) are among the companies that have expanded companion robots into healthcare.

Many technology companies are currently conceptualizing AI-powered surgical robots by leveraging the capabilities of machine learning applications like Google DeepMind, IBM Watson, and others. With AI-enabled robots, less damage will occur, precision will increase, and recovery will be faster.


With the growing amount of digital data and the complication of overseeing a large number of patient records, all end-users under the healthcare umbrella are embracing artificial intelligence. AI-enabled content analytics can enhance providers’ ability to administer effective and efficient treatment protocols by speeding up the diagnosis of patients’ conditions through the use of NLP tools.

Although AI has many proven applications in the healthcare sector, the extent of its implementation in medical practice is still at an initial stage. The healthcare industry is expected to undergo a transformational change as AI adoption increases. It is estimated that healthcare data diagnosis and treatment procedures will improve as a result of the expected growth in the future. The healthcare industry can reap numerous benefits from AI, which has a wide range of applications.

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Image Annotation : overview 2022 used various type of annotation

One of the most crucial phases in creating computer vision and image recognition systems, which entails identifying, getting, characterizing, and evaluating outcomes from digital images or videos, is image annotation.

AI applications frequently employ computer vision, including security, medical imaging, autonomous cars, and others. As a result, image annotation is essential for developing AI/ML in many fields and sectors.

Before we dig deep into the topic, let us discuss some basic nuances of image annotation.

Image annotation

Adding labels to an image is known as an image annotation. These labels are set by the AI engineer and are selected to provide details about what is seen in the image to the computer vision model.

react to section with heart react to section with light react to section with money react to section with thumbs-down The number of labels on an image might be different according to the project. A single label will sufficiently convey the complete image’s information (image classification) for specific projects. Other applications need many items labeled with distinct labels inside a single photograph.

The Process of Image Annotation. How does it work?

Three crucial elements are required to annotate an image:

1- Images

2 – Adding annotations to the photos

3 – A tool for adding annotations to the photos

Finding and training annotators to complete the annotation tasks is the first step in most image annotation initiatives. Although AI is highly specialized, annotating AI training data is not always necessary. While a master’s degree in machine learning is required to build a self-driving automobile, it is not needed to draw boxes around cars in photos (bounding box annotation). As a result, few annotators hold degrees in machine learning.

However, because each organization will have distinct needs, these annotators should get in-depth training on the requirements and norms of each annotation project.

After receiving training on annotating the data, the annotation team will begin working on hundreds or thousands of photos on a platform specifically designed for image annotation. This platform’s software needs to be equipped with all the tools required for the particular kind of annotation to be done.

What methods and techniques exist for image annotation?

There are many different image annotation methods and techniques, but it doesn’t imply you should use them all. Understanding the most popular image annotation methods and techniques along with use cases will help you decide which annotation tool to use for your project and what it requires.

Bounding boxes

Rectangles are drawn around symmetrical items like furniture, vehicles, and packages using bounding boxes. This facilitates object detection and algorithm localization, which is essential for driverless cars.

Self-driving automobiles can navigate the roadways securely with annotations of people, traffic signals, and vehicles. Bounding boxes come in two and three dimensions (cuboids).


Segmentation goes beyond object identification and picture categorization. With this technique, a picture is divided into several parts, each of which is given a label. Since each segment consists of pixels, each pixel has a label, which increases the accuracy of the annotation.

Three categories of segmentation exist:

1 Semantic segmentation

Semantic segmentation involves grouping an image into clusters and giving each group a label. Consider that we have a picture of three sheep. Each sheep will be counted as a separate cluster for semantic segmentation. The sky will serve as the background, which will also be considered.

2 Instance segmentation

Objects’ existence, position, form, and number are determined through instance segmentation. For example, instance segmentation may count the number of persons in a picture. Let’s revisit the sheep illustration. Even though they are given the same name, each sheep will be counted as a different instance in the instance segmentation scenario.

3 Panoptic segmentation

Instance segmentation and semantic segmentation converge in panoptic segmentation. It semantically segments the image and categorizes each pixel, identifying the instances to which each pixel belongs (instance segmentation). Each sheep will be tallied independently in our case, even if all the pixels in the image will be given names.

4 Skeletal Annotation

Skeletal annotation draws attention to body alignment and mobility. Annotators use this technique to connect lines on the human body by adding dots at the locations where the lines articulate. For instance, a line from a point on the wrist to a point on the elbow is then connected to the shoulder, and so on.

This produces a condensed representation of a body’s location that computers can recognize. This approach is used for video in most sports use cases, requiring annotators to precisely identify body postures over tens of thousands of individual frames.

5 Bitmask Annotation

This annotation style permits gaps or disjointed annotations by connecting individual pixels to particular objects.

6 Polygons

The edges of objects with asymmetrical shapes, such as rooftops, plants, and landmarks, are annotated using polygons.

7 Key-points

By placing dots around the target item, such as facial characteristics, body parts, and stances, key points are used to annotate microscopic shapes and details.

To achieve certain annotation objectives, any of the aforementioned annotation approaches can be combined. AI firms have freedom and alternatives when organizing annotation projects as the company can also do customised annotations as per the requirements.

Essential applications and benefits of Image annotation

When employing computer vision (CV) models, image annotation is extremely important for machine learning and artificial intelligence.

Across sectors, the advantages and significance of picture annotation services are becoming recognized. In 2023, the market for AI and machine learning data solutions is predicted to reach $1.2 billion.

Here are some examples of how businesses in various industries use image annotation services to their advantage.

Transportation: CV is used by self-driving automobiles, which are a reality today. Advanced machine learning algorithms must power these vehicles to ensure their efficiency and safety. Automobile makers may create intelligent apps for these autonomous vehicles with picture annotation.

Healthcare: In healthcare, CV can assist increase the precision of the diagnosis and raise the standard of care. With image annotation, CV systems may use CT scans, MRIs, and other diagnostic technologies to focus on patterns and problems like tumors or hairline fractures.

Agriculture: Precision agriculture combines established farming practices with cutting-edge technology to increase profitability, production, and sustainability. CV systems make it feasible to forecast agricultural output, assess plant health, and improve soil quality. In modern agriculture, robotics, GPS sensors, and drones play significant roles.

eCommerce and retail: Annotating images may improve clients’ user experiences and aid them in selecting the best items. When an annotation is appropriately done, each item on the website will have precise descriptions and labels. Annotation ensures that the products are correctly categorized, improving search results. Additionally, it helps to improve visual search. react to section with heart react to section with light react to section with money react to section with thumbs-down


The goal of image annotation is to make the most of technology, and it is something that will never go away. When key AI projects need image annotation, most AI organizations use expert data labeling services.

react to section with heart react to section with light react to section with money react to section with thumbs-down Businesses that provide expert data labeling services, like Cogito or Anolytics, appen, or any other, have skilled experts to help with data labeling and annotation. These companies can tailor their services to your unique needs as they provide seamless project management, quality control, and lower overheads. Originally published at –

Big data analytics and AI importance in media and entertainment industry

The media and entertainment industry is also utilizing the power of Artificial Intelligence (AI) in making the visual content more interactive and interesting. It is helping to serve the audience a data-intensive and personalize automated content making their viewing experience more interesting and entertaining.

While on the other hand, the entertainment industry needs such innovative technology to make the audience experience more enjoyable and user-friendly. AI companies are also consistently working to integrate this technology into various sub-fields of the entertainment sector improving its efficiency.

AI Applications in Media and Entertainment

The application of AI in media and entertainment is witnessing rapid growth especially in terms of distributing and showing visual content. Media companies face challenges while developing the content in high quantity while ensuring the quality, hence they adopt AI to achieve this objective.

And once they achieve their objective, they improve their services enhancing the experience of the customers. So, we have discussed here the use of AI in the media and entertainment industry with use cases and examples.

Personalizing the Content & Recommendations

From music app to OTT platforms, the audio, as well as visual contents, can be personalized as per the preferences and previous experiences. Using machine learning, users behavior and demographic details, music or videos are recommended like what kind of movies they like most.

AI using advanced machine learning algorithms and deep learning networks helping in delivering personalized content to users. And this kind of highly personalized experience for users helps media or entertainment companies increase their user base and offer a better service compared to their competitors.

Online Advertising with Targeted Audience

Online advertisements in the media industry are playing a significant role in branding and business promotions. And AI is used to make online advertising more precise and productive with a targeted audience for higher conversion rates.

The best example is Google Adsense and Adwords that can use the user’s history like what kind of products they were searching for or browsing on web or eCommerce sites. And this kind of AI-based sensing helps AI to show the ads as per the user’s preferences. It helps advertisers to target the right audience and get maximum outputs from the Ads.

Controlling the Online Content Broadcasting

The online media and entertainment industry is running with the help of different types of content. And apart from general topics or common subjects, there is objectionable content broadcasted through TV, Online Music Channels or OTT platforms.

Controlling such content is mandatory from the regulatory authorities. Again AI helps here in detecting and filtering such objectionable contents. AI can identify the user’s age and gender before showing such content or using the automated content moderation service to moderate the objectionable content before broadcasting with the audience category ratings like suitable for children or only for adults.

Classification & Categorization of Content

Online streaming platforms like YouTube or OTT have different genre of music videos, songs, movies or TV shows. And these online platforms are using AI algorithms to show the content as per the user’s preference and choice.

The AI-enabled system can detect the objects, data, visual scenes and other details about the movies like genre, cast and crew members to show them similar content. Implementing the AI in media and entertainment industry can automate the categorization and classification of content for a better users experience.

Meta Tagging Subtitles & Automated Transcription

Content published in the media and entertainment industry needs to make comprehensible to the audience. Hence, AI can help in identifying the videos and other online content to classify them with meta tags and descriptions.

Apart from that movies, music videos and TV shows are transcribed into different languages using AI-based technologies like natural language processing through machine learning and deep learning. The voice of movies is converted into different languages with subtitle and audio annotation to make it understandable to a wider populace for more engagements of the users. Powered by Cogito Blog

Which is better for image classification, supervised or unsupervised classification?

Image classification is actually fundamental task that helps to classify and comprehend the entire image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.

Usually, Image Classification to images in which only one object appears and that is only analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.

Supervised vs Unsupervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.

The classification is the process done with multi-step workflow, while, the Image Classification toolbar has been developed to provide an integrated environment to perform classifications with the tools.

Not only does the toolbar help with the workflow for performing unsupervised and supervised classification, it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.

Supervised Image Classification

Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Data Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.

Unsupervised Image Classification

Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, capability to analyze the quality of the clusters, and access to classification tools

In unsupervised classification, it first groups pixels into “clusters” based on their properties. Then, you classify each cluster with a land cover class.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Which one is better?

No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.

While on the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and label the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.

Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better to make the objects recognized with the better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. And depending on the AI model or machine learning algorithms compatibility, image classification process is followed to classify the images with better accuracy and quality object detection.

How to Find Best Image Annotation Company in India?

Image annotation is becoming the need of AI industry to train the machines with large volume of visual data sets. It is a kind of object lebelling in images making recognizable for computer vision that helps machine learning algorithms to understand the similar objects. And there are various types of image annotation techniques applied while doing this job.

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