Is Artificial Intelligence going to transform Medicine?
Artificial Intelligence in Medicine requires the collation of vast sets of data combined with detailed inputs from doctors and other medical professionals. With a steady agglomeration of data, AI can deliver the possibility of machine diagnosis of diseases and the ideal treatment regimen. Together with all of the data, algorithms can create the best models based on either rules or on inferences.
Initial Interest in the 70s
INTERNIST-1 was designed in the early 70s with a partitioning algorithm and exclusion functions. Prior attempts had been based on statistical models developed by Thomas Bayes and on Pattern Recognition. Ineffectiveness in diagnosing complex conditions led to INTERNIST-1 being redefined with the introduction of a microcomputer as Quick Medical Reference (QMR). Another system that was developed in the early 1970s was Mycine written in the Lisp programming language. Present Illness Program (PIP) and CASNET were the other systems developed in that period.
Revival in the 2000s
After a period of stagnation in the 90s, the turn of the millennium brought technology to te forefront of all business and lifestyle endeavors. This period was marked early on by the defeat of Chess World champions by computers, the victory of computers at popular games and the spread of smartphones and networked homes. Through the 2000s, processors kept getting better and better while also getting cheaper. This brought AI back onto the fast track of innovation.
Peculiar case of Medicine
In medicine, physicians and experts develop their knowledge by a process of slow and incremental learning through the years. No amount of educational brilliance can surpass a doctor who has seen and observed patients and diseases progress, retreat and be treated or overcome the bodily and chemical responses. Now with digitization and standardization, it is possible to let AI systems scour through all the electronic documents and accumulate learning and intuitions. It is but a matter of time before a well-designed system comes into the practice of diagnosis of diseases after going through each and every document that is out there in a clinic, hospital or in way-bigger systems.
Faster evaluation of test/scan results
AI systems can be trained to look into the images that are a fundamental feature of medicine today. When a system looks an image of a scan, it will have the ability to pick up the minutest patterns that are present in the image. These are jobs that usually occupy the time of the doctors and which will be freed up by relying on AI. Further, the job is not only done fast but also probably better, which makes it an ideal situation when doctors with experience are not available.
Building the system
The more wider the system that feeds into the AI machine, the better that machine learning can come up with better outcomes. When physicians train the AI system, it gets the right directions in which to drive at as it goes about bettering itself each day of its evolutionary existence. Even with the earliest systems of the 70s, it was observed that improvements are dramatic when the early errors are corrected and the system is pointed in the way to go.
Exciting and futuristic probabilities with AI
Epidemics are one of the main target areas that AI can address with great effectiveness. When it comes to handling multiple variables and analyzing them comprehensively, an AI system can help develop just-in-time alerts for healthcare systems. Another equally futuristic possibility is that of detecting when diseases are going to occur in risky sections of the population. This makes disease treatment a highly-enhanced area of medical operation wherein there is no surprise at all when a disease is found in a person.
A whole new outlook on disease
In the dramatic future that AI can deliver within this decade itself, diseases will end up being similar to greying of hair or wrinkling of skin as they are known well in advance. This can transform the very model that medicine is based upon, from that of having patients who have no clue on what is going to happen next to one where patients know about its imminent arrival and all that can be done to stave it off even before it has reared its head.