Unfortunately, we’ve had plenty of misdiagnoses in all of medical history. It’s quite a startling statistic considering just how many have had their health and well-being been put into jeopardy by such mistakes.
Therefore, it’s quite important to diagnose diseases fast and accurately. This way, the patient stands a much better chance of recovery because of early intervention and the subsequent provision of appropriate treatment.
But this isn’t always smooth sailing.
Given the complexities of, say, a blood test for bacterial vs viral diagnosis, delayed or inaccurate diagnoses, summarily dismissed as diagnostic errors, may occur for any number of reasons. Medical negligence, a test’s slow turnaround time or medical malpractice are likely culprits. These errors may delay or altogether prevent the prescription of appropriate treatment to the patient.
Also, a delayed or inaccurate diagnosis may lead to the misuse or overuse of medicine to treat the infection. For this reason, antimicrobial resistance (AMR) has been cited as one of the many unfortunate consequences of diagnostic errors. AMR, according to the World Health Organization (WHO), occurs when bacteria and viruses evolve to no longer respond to medicine. It makes infections a lot harder, or even impossible, to treat, further increasing the risk of severe illness and death, not to mention the spread of disease.
In light of these dire ramifications of diagnostic errors, artificial intelligence (AI), and more specifically, machine learning, is increasingly being applied in medical diagnostics in efforts to speed up results and improve diagnostic accuracy.
In other words, a machine learning diagnostics company makes diagnostic tests that are more reliable and a lot safer to use.
What is Artificial Intelligence?
To be able to fully grasp how artificial intelligence is set to facilitate safe and automated diagnostics for all, we first have to understand what artificial intelligence at its very core really is.
Artificial intelligence is the concept of creating intelligent machines that mimic human intelligence. It is simply creating machines that not only perform the slow monotonous tasks we’d like to avoid but machines that can also do exactly what we do, if not better.
Machine learning, on the other hand, is ideally a subset of artificial intelligence that involves training algorithms, using lots of data from observation and real-life scenarios, to make sure these ‘intelligent’ machines learn how to think and act like we do.
Machine learning excels at identifying patterns and extracting insights about almost anything given lots of data.
So, How Can an AI Diagnostics Company Apply AI in Medical Diagnostics?
If we’d like to improve patient outcomes, it’s pretty clear we need to address the key clinical challenge of delayed and inaccurate diagnoses.
So, to do this, a machine learning diagnostics company can develop quality diagnostic equipment complete with in-built algorithms that study how expert clinicians interpret test results. It then uses this knowledge to automate future routine analyses.
Frankly, this is how machines get to be so good at diagnosing diseases considering just how much conventional tests are prone to human errors.
Aside from their rather novel approach of using the immune system as a sensor for disease, MeMed, a leading AI diagnostics company, is already at the forefront of using artificial intelligence to improve the diagnostic capabilities of their diagnostic devices.
As opposed to trying to find pathogens as most conventional tests often do, the MeMed BV test, in particular, is a blood test for bacterial vs viral diagnosis that relies on the immune system itself to solve the clinical dilemma of whether a patient is suffering from a bacterial or a viral infection.
It measures and computationally integrates the levels of 3 specific immune system proteins to tell bacterial and viral infections apart, which is no mean feat by the way.
Conclusion
Although it’s yet to make its biggest impact yet, artificial intelligence offers the potential to dramatically reshape the healthcare landscape as we know it. With fast and accurate diagnosis, we’ve actually got a pretty decent chance of improving patient outcomes and saving lives.
