Nowadays, we use many telemetry products and gadgets to collect increasingly large and complex datasets daily. It is impossible for humans to process and analyze such a high volume of data quickly enough to understand and utilize it in better care delivery. This is where Artificial Intelligence (AI) and Machine Learning (ML) algorithms come into the equation. Based on the predictions by Global Market Insights, the Healthcare AI market is projected to surpass $34.5 billion by 2027.
When it comes to healthcare, AI and ML are powerful tools that are cautiously being introduced to different aspects of healthcare to help with the speed and interpretation of data processing and automatization of certain processes or parts of processes. AI has already found application in many healthcare areas like radiology and cancer diagnosis/detection, as well as in treatment of mental health conditions. For example, providers are already using Google machine learning algorithm to diagnose and treat diabetic retinopathy.
In addition, AI finds application in other cutting-edge areas of healthcare like at-home patient monitoring, medical imaging & diagnostics, precision medicine, robotic surgery, and drug discovery. AI can assist in remote patient care by noticing any changes in patient data that may be less distinguishable by humans. Obtained data can be compared to the previously collected datasets using AI algorithms that alert physicians if there are any abnormalities, saving the time of the provider and improving patient care. For years now, AI has been used in the analysis and review of radiology images to help with the early detection of different cancers, speeding up the process of establishing diagnosis up to 30 times with 99% accuracy. AI-assisted robots can execute parts of different procedures with supervision – some of their surgical tasks include precision cutting or stitching. Such a vast application justifies the growing interest and investments in AI and ML-based technologies.
There is a lot of potential for the application of AI in healthcare – however, perfecting AI algorithms for medical application takes time. For example, IBM’s Watson for Oncology took about six years to perfect, and it will be able to diagnose 12 cancers that account for 80% of cancer cases in the world.
These new technologies are introduced to help improve and expedite existing processes and are in no way set to completely replace human providers. AI can help get rid of repetitive parts of physicians’ jobs, leaving more time for patient care. It is important to emphasize the benefits of combining the powers of AI and physicians to improve diagnostics and predictions as we are yet to see what we can achieve with it.
Vivadox provides tailored AI solutions for a variety of clinical use cases including image recognition and automation. If you want to learn more about how Vivadox can help you implement AI for your use case please reach out to our team at email@example.com