How Medical-AI Could Play A Bigger Role With the Aid of Data Labeling Service?
With the development of the social economy and the improvement of residents’ living quality, the demand for medical services is growing rapidly.
However, due to a variety of complex factors, the medical industry is facing many pain points. In terms of supply, the medical sector has long been faced with problems such as uneven regional distribution, and shortage of experienced doctors. On the demand side, with the acceleration of the population aging process, the demand for medical resources expands significantly.
Intelligent Medical Treatment
The best way to solve the problem is the introduction of intelligent medical treatment, which has highlighted the medical industry incisively and vividly.
AI-assisted diagnosis and intelligent customer service have effectively liberated limited medical labors and brought an improvement in the service experience.
In the post-pandemic era, the combination of AI and the medical industry is expected to usher in a new era of development.
1. Image recognition
Under the traditional medical operation mode, all medical images are previewed by doctors, and diagnosis is made accordingly.
However, the diagnosis speed is relatively slow, and it completely relies on a personal capacity, which requires a large number of professionals in certain fields. The application of image recognition technology will solve the problem. With the help of technology, the lesions can be automatically identified and labeled in the early time, and the ones that cannot be found by naked eyes can be pointed out, which helps doctors diagnose with accuracy.
Also, compared to manual diagnosis, AI image recognition is available 24 hours a day, which greatly improves efficiency.
2. Remote consultation
Covid-19 is highly infectious and there is a risk of cross-infection in hospital visits. To avoid person-to-person contact and realize medical consultation within doors, remote consultation and online customer service will play a key role.
In practical cases, doctors can use voice recognition technology in replacement of traditional handwritten medical records, which greatly reduces their burden.
In the online consultation scenario, while the user enters the symptoms, the AI system will automatically recognize the text, completing a series of tasks such as speech tagging and information extraction, etc. By searching in the database, information matching will be realized immediately. The diagnosis will be completed based on the reference.
A More Important Role in the Post-pandemic Era
To a large extent, the application of AI technology has alleviated the problems of medical resource shortage, uneven regional distribution and improved the overall operating efficiency of the medical system.
In one sentence, intelligent medical care is expected to play a more important role in the post-pandemic era. However, AI technology is currently playing a more complementary role in the medical field, and cannot completely replace the role of doctors.
Common Labeling Tools in Medical Industry:
- Regional Segmentation
- Key Points
- Medical Records OCR
Common Labeling Types in Medical Industry
- Medical Text & Documentation
- Point Annotation of Sport Player
- Athlete Behavior Monitoring
- Cells and Tissues Counting & Annotation
- SCANs, CTs, MRIs Semantic Segmentation
Regarding speech recognition, the models need NLP technologies, such as information extraction, voice tagging, and noun classification.
Behind Medical-AI: Data Annotation Service
From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.
Accuracy
High-quality training data will maximize the efficiency of artificial intelligence, while low-quality AI data will be not only impossible to improve efficiency, but also will hinder the evolution of artificial intelligence to a certain extent.
Data Security
Different countries have issued corresponding laws and regulations for data security. Medical data is quite sensitive as it is related to personal privacy. Medical companies always find it hard to acquire unlabeled data and are concerned about data security.
Another security concern is a data leak. Once data is transmitted, it is possible to get copied. Customers are worried that the data will be directly copied and sold to competitors.
In conclusion, except for legal norms, data security is essentially a matter of trust.
End
Outsource your data labeling tasks to ByteBridge, you can get the high-quality ML training datasets cheaper and faster!
- Free Trial Without Credit Card: you can get your sample result in a fast turnaround, check the output, and give feedback directly to our project manager.
- 100% Human Validated
- Transparent & Standard Pricing: clear pricing is available(labor cost included)
Why not have a try?
Relevant Articles:
1 How Data Training Accelerates AI into Medical Industry?
2 How Data Labeling Service Empowers Medical Industry?
3 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
4 8 Common Data Annotation and Labeling tools
5 No Bias Training Data — the New Bottlenecks in Machine Learning
