In recent years, the idea of Artificial Intelligence has revolutionized the healthcare scene, and data annotation is at the forefront of this revolution, persistently driving progress in the development of Healthcare AI through continuous innovation.
In this article, we will be discussing:
- Data annotation and its role in the development of AI in Healthcare
- The Usage of AI in Healthcare.
Data annotation and its role in the Development of AI in Healthcare
The quality of Artificial Intelligence has improved so much in recent years, thanks to continuous innovations in data annotation. Several aspects of healthcare like Virtual Assistance, Diagnosis, Medicine, Surgery, and Patient Follow up use different types of AI to make the process easier.
Today, developers train AIs for years to improve their accuracy and functionality. Therefore it is commonplace to have AIs that can prescribe, infer and analyze data and give valuable insights.
Some challenges remain in the application of AI in health care; however, since AI data annotation can draw the line between life and death, data reports and scans have to be annotated by specialists. Training and recruitment of these professionals could be tedious and expensive.
Usage of AI in Healthcare
Out in the field, the influence of AI waxes even stronger. Today, numerous practical uses of AI statistically produce comparable or faster results than human intervention. Some of them are;
- AI-assisted robotic surgery: Similar to how AI has been introduced to make driving safer, similar processes are being instituted to make surgery safer and quicker. In some surgical procedures today, AI-assisted surgery produced better results than human intervention. A study found that AI-assisted robotic procedures resulted in 5x fewer complications than when surgeons went in alone.
- Virtual Nursing assistants: Unnecessary hospital visits waste patients' resources needlessly. Improvements Sentiment/Intent Analysis, Entity Recognition make it possible for AI to produce tailored responses by being available to answer patients' questions 24/7. Also, AIs utilize remote monitoring sensors to accelerate the identification of illnesses, schedule appointments, monitor health statuses, and provide medication reminders to patients.
- Aid clinical judgment or diagnosis: Thanks to Medical image annotation for diagnostic support, Specialists can identify medical issues more accurately. Human error is a recurrent issue in various types of diagnosis, such as CT scan analysis. However, the inclusion of AI in the process has seen improvements in speed, accuracy, and costs associated with analyses.
- Medicine: With the Aid of Machine learning algorithms, Healthcare providers can identify chemical and biological interactions more efficiently, paving the way for the rapid development of drugs for various new issues.
Conclusion
A new age is upon us, and this new age leaves no space for ineptitude. AI has been instrumental in helping healthcare providers to care for their patients better and faster than ever before. Several frontiers in health care such as medicine, diagnosis, patient management, and surgery are conquered regularly. The future promises even more as the quality of data annotation and machine learning continue to improve.
