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I have a vision, a vision that holds the potential to revolutionize medicine as we know it. I want to introduce Artificial Intelligence to the field of medicine, and I want you to be a part of this transformative journey. What does this mean for all of us? It means a creation mathematically proven, alternative medicine of the future. Can you imagine all medical disciplines in digital processing, where we will be able to do whatever we want. You will be able to check the probability of your diagnosis and see all alternative diagnoses. We will be able to calculate the optimal way of diagnostic and treatment for everyone. We will be able to fully control our health.
Why should you believe in this vision? Because I have meticulously crafted the three fundamental components of Medical AI.
So, it’s time to make a definition of the Artificial Intelligence.
By my definition, AI is a computer program that embraces at least one scientific discipline and performs the intellectual work of a person mathematically accurately or with greater probability than a human!
Any artificial intelligence consists of three main parts: the algorithms on the basis of which it works; a matrix or knowledge base; and machine learning. Combination of those components in one intelligent computer program is medical AI. The previous title was Global Diagnostic and Treatment System (GDTS) which I launched in 2003.
One of my early programs (1993) could automatically make diagnosis related to the colon diseases. I have created an algorithm that determines the disease by the sum of likelihoods of the entered symptoms? And then I introduced the concept and the term of this algorithm and called it as “Algorithm of Syndromic Diagnosis”.
When I was developing a Software Requirement Specifications (SRS) there were many databases in it, such as: Layouts of Medical Records; database of patient’s data; data base of medical records; database of colonoscopies; database of colon’s diseases and their symptoms; algorithms; database of doctors; database of medical facilities etc.
To avoid confusion in definitions of medical databases, I introduce the term “Knowledge Base” for all inbuilt data such as diseases and their symptoms.
In order for the program to draw conclusions or predictions based on the input data, we must compare this data with the matrix in which the original data is stored. This matrix is the Knowledge Base of medicine.
Knowledge Base has been submitted on International Congress of Digital Medicine Nanjing – China in 2016 (“Data base of Illnesses and symptoms and Universal Algorithm forming it structure”).
If a program can make predictions of the diagnosis based on the Knowledge Base and input data, therefore the Knowledge Base can be considered by the main component of Artificial Intelligence.
Second component is algorithms. There are different types of algorithms depends of the tasks which they perform. Some algorithm can make conclusions and predictions based on input data. Algorithm of Syndromic Diagnosis shows a direction of diagnostic and calculate the probabilities of diagnosis. Differential Diagnosis Algorithm can make any kind of diagnosis with probability up to 100% even starts from general blood test, for example.
Machine learning and deep learning are frequently mentioned in conjunction with artificial intelligence. It is going to be a third component of AI. But we should think carefully about whether to include a self-learning function in Medical AI due to the possibility of duplicating a human factor. This should be the subject of a special scientific study. If we apply this situation to medicine, every person wants to get a 100% reliable diagnosis and not depends on the human factor or any kind of mistakes.
Alternative of machine learning is Cross-Disciplinary Deep Analysis System. I created the Cross-Disciplinary Deep Analysis System for science and practical purposes, for example, for search of individual optimal ways of diagnostic and treatment and so on.
Mostly of components were involved into the “Server of Information and Analytical Medicine” (SIAM). SIAM was submitted on 10th International Symposium on Maritime Health 2009 Goa – India. The knowledge base consisted of major medical disciplines and toxicology. There was an idea to demonstrate the operation and capabilities of the server on different types of platforms. That is why the Knowledge Base has been installed on different types of platforms such as SQL, Oracle, MongoDB. Accordingly, server performance for different tasks differed.
I submitted the SIAM on many symposiums for a rather long time.
And also, the SIAM was submitted on Global Summit on Telemedicine & e-Health 2015, Houston, USA: “Server of Information and Analytical Medicine, Ukraine”.
Also, there were other interesting solutions. On base GDTS has been created web service with fully automatic consultations. This web service determined the patient’s diagnosis, partially filled out the medical record and had to report the results to the medical facility and assign an appointment.
In fact, the Global Diagnostic and Treatment System can be considered as the earliest prototype of medical AI.
By the way, the idea of online consultations turned out to be quite successful and viable. Online consultations as a web service of doctor’s consultation in real-time mode has been submitted on International Congress of Digital Medicine Nanjing – China in 2018.
But the time for demonstrations has already passed.
Unomay.com offers solutions based on medical artificial intelligence for any type of medical business.
But there are still interesting challenges. In my vision, the next step in Unamai’s evolution will be the creation of a mobile application of medical AI.
Unamai’s mobile application is going to be the most advanced system in medicine for the benefit of all mankind! All backers and stakeholders welcome! And let’s do it!
March 2023
