Why are deep learning, machine learning and AI so important in telemedicine and other types of medical services?
One of the biggest and most primary of these synergies is in document review. IBM is revealing how its Watson Health program is able to analyze millions of pages of medical information within seconds, and draw conclusions that can be used for diagnosis, comparison and more. The enormous power of machines to handle large volumes of data is combined with analytical and decision-making prowess in machine learning and artificial intelligence technologies.
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Beyond just dealing with information, though, machine learning and artificial intelligence can also bring new capabilities to patient examination. For example, in radiology, machine learning algorithms can look at radiology scans and other resources to find evidence of outcomes and realities that can guide human decision-makers.
As another formative example of the power of machine learning and diagnosis, National Institute of Health resources document automated analysis of retinal imaging, which can help to detect certain types of sight loss linked to diabetes.
In addition to all of the above, which is very substantial and groundbreaking functionality, there is also a range of ways in which machine learning and AI can assist with the daily realities of telemedicine. From scheduling to consultation and examination to diagnosis to billing, these types of technologies will be able to automate the telehealth process.
In early telemedicine, the concept was relatively simple – instead of being physically present to do house calls or to consult or examine a patient from remote areas, doctors used videoconferencing and related technologies.
However, with machine learning and AI, doctors will be able to combine that with decision support tools – the automating technologies will do a lot of the work. Doctors will review it and sign on – instead of being only supported by videoconferencing, doctors will also be supported by key assistive technologies that are thinking and learning on their own. This will dramatically change the field of telemedicine soon and rather permanently.
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- Machine Learning
- Artificial Intelligence
- Deep Learning
- Decision Support System
- Clinical Decision Support System
- Video Conferencing
- IBM Watson Supercomputer
- Cloud Computing
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