Whether for vehicles, industrial equipment, or mental health conditions, diagnosis is crucial to resolving problems but very difficult to do remotely. Thankfully, however, new advances in technology and artificial intelligence have made remote diagnostics possible.
Not every person or vehicle can easily make it to a clinic or repair shop when a problem occurs. In fact, in some cases, such as on a space station or in quarantine, it is impossible. In these situations, remote diagnostics can resolve critical issues quickly—or even save lives.
To create a useful remote diagnostics system, start with baseline data on the equipment or human body. To wit: fuel levels, repair schedules, blood oxygen levels, risk factors in medical history, etc.
The most important external data source for a remote diagnostics system is geolocation data. This not only may indicate the source of a problem like, for example, local disease outbreaks, but also the closest clinic or repair shop that can send help.
Additional useful external data includes a tech interface that enables patients or equipment operators to speak to healthcare practitioners or mechanics.
There are two main challenges to remote diagnostics. First, the fleet manager receiving constant data updates may have trouble interpreting the information. In particular, determining an upcoming problem from an imminent problem is difficult.
Second is the disconnect between diagnosis and solution. While alerting the closest repair shop to the exact problem may save crucial time for companies, this may not always be the case.
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