Everywhere there is talk of the advantages that artificial intelligence (AI) can have for our daily lives and among its possible applications, one is the interest of health professionals in applying it to daily clinical practice. But, often, it is not specified when and how these professionals should use, or rather, in which cases their application should be prioritized.
Health professionals can apply AI in their clinical routine, whether in emergency medicine or chronic care by means of decision algorithms. To make it easy, they ask: is the patient experiencing pain anywhere? they will then move on to a second question; Have the patient vomited? Do they have diarrhoea? Does the patient have a fever? How much? Do they have an extended stomach? And so on.
According to the answers, a clinical assumption or diagnosis will be made. This would be an easy decision tree to copy for AI applications as one example. But there are decision algorithms much more complex, where current and previous clinical data are mixed, serum biology (also called clinical tests), imaging tests (ultrasound, radiographs), electrical records (electrocardiograms, electromyograms) and, more recently – and in some cases – genomic analysis. This is where AI or artificial intelligence comes in healthcare to make the lives of doctors and medical professionals much easier.
Artificial Intelligence and Decision Making
With the complexity and abundance of data that we already have, AI will be useful to process them and facilitate the diagnostic and therapeutic decisions. This would be a close and feasible application and is already useful in some partial decision making. As for the therapeutic decision, the pharmacogenomics and the nutrigenomics analyzed through AI tools help a lot in finding the right pharmacological treatment or diet. There has been very interesting work where, depending on the pathological pattern of renal tumours, artificial intelligence determines which patients are at high risk of infections and which will are and. With that, specific measures will have to be taken.
AI and Clinical Image Analysis
There are other applications of AI that have proved it very useful when it comes to image analysis. Here the AI is able to read much better than people in retinography (analysis of lesions in the retina, number of microaneurysms, size of the microvessels, etc), an echocardiogram, a CT scan, a resonance, and even a simple x-ray. The analysis is so powerful that it can be obtained through AI methods. Some doctors already think that as with AI as an image reader, their future professional activity will be very limited. What will be their role them? Supervisors? Even more. Imagine we have to read the microscopic results of a tumour. Should we accept the diagnosis of carcinoma types of carcinoma because the machine tells us? Is it more accurate than human knowledge? We can’t say for sure. It is safe to say that in this field, artificial intelligence is a reading tool that has a great future in medicine.
Conclusion
Let’s not forget that there are many forms of artificial intelligence. Some of them, such as the one for neural networks have been the subject to great debate. Others are already so effective that they have been used in clinical practice. One day AI will help assist us in the outpatient or hospital and, without realizing it, the diagnostic and therapeutic decision will be made by artificial intelligence.
Source: https://www.klusster.com/portfolios/matheiurobine/contents/15723
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