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 lack of resources, 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.
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 about 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 leapfrog development.
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 AI’s image recognition technology will effectively solve such problems.
With the help of image recognition technology, the lesions can be automatically identified and labeled even in the early time and the ones that cannot be found by naked eyes could be pointed out, which helps doctors diagnose with accuracy.
Also, compared to manual diagnosis, AI image recognition is available 24 hours a day, which will greatly improve 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 accuracy matching will be realized immediately. The diagnosis will be completed based on the reference.
To a large extent, the application of AI technology has alleviated the problems of medical resource shortage and 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.
The application of image recognition technology is inseparable from the support of data annotation technology. The training of the image recognition algorithm model needs the support of large scalable annotation data sets, and image annotation and face key point annotation are common annotation types.
Regarding speech recognition, the models need NLP technologies, such as information extraction, voice tagging, and noun classification.
Data, Algorithms, and Processing are Three indispensable Elements of AI.
Data is the starting point. It makes sense for AI to start with data and take advantage of learning itself. In scalable data and the high-speed era, it is very convenient to use data to train artificial intelligence.
The strength of an AI system depends on the algorithm model and the quality and quantity of training data. It is showed that many AI companies use similar algorithm models, therefore, the quality and quantity of training data play a key role. In fact, getting high-quality labeled data is the toughest part of building a machine learning model. If the data quality is unqualified, the algorithm model cannot be well developed, AI company needs to label the data again. Timing is important, once, behind the schedule, the product may be overtaken by competitors.
Different countries have issued corresponding laws and regulations for data security. For example, according to the EU GDPR, data cannot leave the EU region.
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 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.
ByteBridge.io, a human-powered data labeling tooling platform with real-time workflow management, providing flexible data training service for the machine learning industry.
Data quality guarantee
1 The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
2 All results are thoroughly assessed and verified by a human workforce and machine
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
In addition, we can provide personalized annotation tools and services according to customer requirements.
We comply with principles and rules in each region and we respect data the way your company does.
- The CEO of the company supervises data management as a DPO (Data Protection Officer)
- According to the guideline, if there is data leakage, we will inform the customer within 72 hours
- GDPR personal privacy and data protection regulations compliance
- Workers location, process, and authority restriction
- No original data leak as the data is compressed and preprocessed
- Support private cloud and privatization deployment
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
1 How Data Training Accelerates AI into Medical Industry?
2 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?
3 8 Common Data Annotation and Labeling tools
4 Data Annotation Service and Its Key Advantage — Flexibility
5 No Bias Training Data — the New Bottlenecks in Machine Learning