Improved patient care, accurate diagnosis, and faster drug discovery have become more important than ever in the wake of the Covid-19 pandemic. The use of artificial intelligence, machine learning, and computer vision applications has increased, and this is rapidly transforming the healthcare industry. AI and ML applications are granting superpowers to healthcare professionals in areas like medical imaging and diagnosis.

However, AI and ML models require huge amounts of accurately labelled training data to scale and perform at the desired levels. Regrettably, 80% of all healthcare data is unstructured and not easily available for processing. This not only limits the quantity of usable data but also hampers the decision-making abilities of healthcare organizations.

To fulfill the data demands of AI and ML models, data annotation companies offer specialized services for medical annotation. This article discusses data annotation types, data annotation examples with their applications, some use cases, and how better annotation in computer vision is beneficial to the entire healthcare industry.

The role of data annotation in the development of healthcare AI and the healthcare industry

A continuous increase in the accuracy of data annotation has radically improved artificial intelligence models and systems in recent years. AI in healthcare has proven beneficial for both patients and healthcare professionals. It contributes to tasks such as virtual assistance, diagnosis, medicine, surgery, and patient follow-up, raising efficiency and reducing operating costs. Despite this progress, the lack of accurate data annotation remains one of the biggest challenges for greater application of AI in the healthcare industry.

Like other industries, the healthcare industry generates loads of data every day. Administrative, laboratory, financial, insurance, medical history and all other segments generously contribute to this colossal data generation activity. The USA itself produces over 1.2 billion medical histories annually, which is increasing by 48% year on year.

For AIs to analyze data accurately, deduce facts and reasons, infer insights and prescribe courses of action, they need the right training. Annotating data to train AI models in the healthcare industry requires specialists because any lack in the quality of AI data annotation can prove life-threatening.

The global data annotation service industry is expected to expand at a compound annual growth rate (CAGR) of 26.6% from 2022 to 2030. This growth is mostly driven by the increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors.

Application of computer vision in the healthcare industry

Like other industries, healthcare also produces enormous amounts of data. As we all know, the more accurately annotated the data, the better computers will be able to see, understand, and analyze visual data.

Research suggests that image annotation contributes to 90% of all healthcare input data. However, it begins with and depends on, accurate image annotation for both diagnostics and robotic surgery. For this, 3D cuboid annotation, polygonal segmentation, bounding boxes, and semantic segmentation have to be conducted extremely accurately. Otherwise, AI will not be able to identify objects with the high precision required in healthcare.

Let’s have a look at the top computer vision applications in healthcare industry

• Blood loss measurement – Computer vision empowers doctors to measure blood loss during childbirth, surgeries, and injuries by leveraging AI-powered tools to analyze images of suction canisters and surgical sponges.
• Cardiology – Computer vision tools are trained to detect anomalies, visualize arteries, detect a flow of blood, determine cardiac MRI fluctuations, electronic segmentation, and much more.
• Tumor detection – Computer vision makes the tumor detection process less cumbersome and time-consuming. It detects brain tumors rapidly and the severity of their spread and helps in saving a patient’s life.
• Cancer Detection – Computer vision effectively and accurately differentiates between cancerous skin lesions and non-cancerous lesions.
• Combating Covid-19 – With computer vision applications helping with the diagnosis, control, treatment, and prevention of Covid-19, we will hopefully reach a stage of zero Covid-19 cases.
• Diagnosis – thermal image annotation – Thermal image annotation helps computer vision extensively analyze and detect breast cancer and other diseases at early stages. While thermal images can only display infrared energy emitted by a tumor, annotating thermal images helps identify individuals with more than normal temperature.
• Robotic surgery – AI, in its infant stage, gifted the lane assistant and collision detection to the first generation of autonomous vehicles. The next step is computer vision-driven autonomous surgery. Video annotation, lesion detection, and phase identification are all set to make surgeries safer and surgeons better.

Some more AI/ML applications in healthcare and types of data annotations used for them

Precise annotation is imperative in creating high-quality training data for AI-enabled healthcare solutions. A lack of accurately annotated data makes it difficult for AI or algorithms to access all of their patients’ historical information. It can cause false diagnoses and incorrect treatments. Here are a few applications of some AI common in healthcare and the types of data annotation associated with their development.

1. Virtual assistants

AI application for 24*7 virtual nursing assistance fast-tracks identification of disease, continuously monitors health status, schedules medical appointments, and provides medication reminders. It is beneficial to both doctors and patients.

Type of data annotation:

• Key point annotation
• Facial recognition
• Gesture recognition for sensors
• Data extraction from patient wearables

2. Conversational bots

These help everyone right from patients and doctors to nurses, clinical and hospital staff. Such AI-based solutions can check symptoms, escalate emergency cases, schedule doctor appointments and even support individuals suffering from Alzheimer’s.

Type of data annotation:

• Entity recognition
• Sentiment analysis
• Intent analysis

3. Diagnostic support

Application of artificial intelligence brings to the table a wide plethora of MRI, X-ray, CT scan, and other analyses. It not only eliminates human errors in CT scan analysis but also considerably improves pace, accuracy, and analysis costs. During the recent pandemic, AI solutions have assessed chest CT scans to successfully detect pneumonia caused by Covid-19. It can perform embryo classification more accurately and, in less time, than embryologists.

Type of data annotation

• Medical image annotation, including MRI, X-ray, and CT scans
• Accurate embryo classification for IVT treatment

4. Medicine – pattern recognition for drug development

Machine learning models and AI assess voluminous data from patents, patient records, clinical trials, and research papers to advance the search for biological and chemical interactions. AI solutions generate billions of inferred and known relationships between biological entities, such as symptoms, genes, diseases, tissues, proteins, species, and suggested drugs in no time. It has improved drug development and helped new pharmaceuticals/drugs to reach the market faster.

Type of data annotation:

• Natural language processing to recognize entities
• NLP used to understand relationships between factors
• NLP used to identify and classify attributes

5. Data extraction for personalized drug treatment

AI solutions have proven their worth when it comes to finding an appropriate drug combination and a dosing strategy for treatment for cancer. AI models collect, analyze, and assess data and information from the clinical trials of a comparatively large number of individuals and are not limited to sample populations. It helps doctors to make superior drug and dosage decisions while administering multiple drugs simultaneously to patients.

Type of data annotation:

• Digital radiology
• Labeling bio-images (MRT and CT scans)
• Electronic medical records (EMRs)
• Health claims data from insurance companies
• Data from wearable sensors
• Data from mobile health applications

6. Chatbots for patient follow-up

Diagnostics, patient engagement outside medical facilities, and mental health are three arenas where AI-run chatbots have proved extremely useful. Healthcare service providers and even medical assistants today leverage AI-enabled chatbots to not only cut costs but also to simplify patient care.

AI-enabled chatbots help hospital inmates and patients navigate the maze of medical assistance, whereas post-discharge care chatbots help increase patient safety and engagement.

Type of data annotation:

• Entity recognition
• Dissecting text and audio in medical records
• Dissecting digital documents and clinical trial data
• Intent and conversation analysis

AI and ML are the future of the healthcare industry

In the coming years, data annotation is expected to play a pivotal role in scaling up AI and ML solutions for the healthcare industry. AI-backed machines will use machine vision or computer vision and employ medical imaging data technologies to identify injuries and assist medical practitioners. It will help them automatically generate reports after the medical examination of any patient. The screening of gigantic databases of CT scans, MRI, and X-ray images will become easy with AI to determine injuries and potential treatment options.

Conclusion

However, none of these would be possible or viable without accurate data annotation. AI and ML companies are striving to hire and invest in specialized data annotation service providers that can help them with accurately annotated data. Even data annotation companies are looking forward to using AI data annotation capabilities on medical datasets. This will enable them to deliver more scalable and flexible medical data annotation solutions that are HIPAA-compliant and adhere to adequate security protocols.