Future of Pharmacy
Artificial intelligence is intelligence demonstrated by machines whether they are robots or algorithms that’s the gist of it.
The three major levels of AI the first level is artificial narrow intelligence or it refers to a computer’s ability to perform a single very well-defined task extremely well like playing chess more accurate than humans the second level is artificial general intelligence , it refers to a machine that can match a human being’s cognitive capacity that has the ability to understand and learn any complex intellectual tasks that a human being can. In medicine this can be the perfect assistant to physicians or even more and the third level is artificial super intelligence that refers to a machine surpassing the cognitive capacity of all humanity.
Machine learning is the method where an algorithm learns from data identifying patterns and making decisions with minimal or no human intervention.
Supervised learning is used when we can precisely define the task we want the algorithm to learn based on data that we already have let’s take the following example we have two sets of medical records of patients group a and group b in one set we have family history lab markers and other details with the diagnosis in the other set we have the same kinds of data but without the diagnosis. So we would like to build a model that can learn to assign the right diagnosis to patients in group b based on the associations and labels. The algorithm learns about in group a it’s like learning be the teacher because we know exactly what the algorithm should learn.
Unsupervised learning is like learning without a teacher we have a group of patients with different sets of data but we do not know their individual diagnosis. We build a model then try to cluster patients based on similar attributes such as the symptoms they presented with their lab markers or age and gender.
We might learn new associations we have not looked at before in summary. We devised certain rules, let the algorithm learn by itself and we do not modify the algorithm based on the outcome. Reinforcement learning is like the teacher is only able to give feedback after a series of actions not for each item as he does with supervised learning. The model starts performing the task only knowing some basic rules and after failing or succeeding in completing the task. The teacher weighs in to push it to use the winning strategy, the program can build its own experiences as it performs the task more and more. It is similar to how we train dogs when the dog performs or tries to perform a task. We only give it a treat if it perform well.
Deep learning it’s a vastly different beast both a subset of machine learning and an entirely different approach to how AI should think. It uses a layered structure of artificial neural networks that is inspired by the neural network of the human brain while the other methods are better with data organized into a spreadsheet and could perform narrow tasks very well. Can be used for more complex tasks and it has the capacity to process images sound and other high dimensional data.
It can advance or outright revolutionize medicine at every level there are many tasks within medicine where we human beings will not be able to compete with AI . The big data coming from studies and have all that knowledge readily available to its human counterparts with that vast knowledge deep learning algorithms, will be able to design treatment plans in oncology. These will analyze huge data sets genomic profiles and combine them with attributes from a patient’s medical file to identify targeted personalized treatments. Those treatment plans will include precision medicine unlike the classical way where drugs and treatments are based on the needs of the statistical average person. Will be able to analyze how your body would react to a treatment plan and advise accordingly before you take any medication is one of the biggest advantages.
Potential to disrupt the pharma industry
It will take even more time for medical professionals to trust AI with a medical diagnosis, this should be taken into consideration when adopting the technology into the healthcare setting.
The ultimate fear is losing jobs replacing humans in healthcare and consequently losing the art of medicine. AI won’t take the jobs of physicians and won’t monopolize medicine. All the tasks that will be taken over by AI and autonomous systems would be freeing up medical professionals to finally fulfill the mission they signed up for to help patients with compassion creativity and care.
The art of medicine would be when an AI will find a treatment which haven’t found before the real. Will be able to reverse engineer and understand how it came to that conclusion this is going to be the biggest challenge physicians have ever faced.
