Motivation As a result of the worldwide health care system digitalization trend, the produced healthcare data is estimated to reach as much as 2314 Exabytes of new data generated in 2020.
The ongoing development of intelligent systems aims to provide better reasoning and to more efficiently use the data collected. This use is not restricted retrospective interpretation, that is, to provide diagnostic conclusions. It can also be extended to prospective interpretation providing early prognosis. That said, physicians who could be assisted by these systems find themselves standing in the gap between clinical case and deep technical reviews. What they lack is a clear starting point from which to approach the world of machine learning in medicine.
Methodology and Main Structure This article aims at providing interested physicians with an easy-to-follow insight of Artificial Intelligence (AI) and Machine Learning (ML) use in the medical field, primarily over the last few years.
To this end, we first discuss the general developmental paths concerning AI and ML concept usage in healthcare systems. We then list fields where these technologies are already being put to the test or even applied such as in Hematology, Neurology, Cardiology, Oncology, Radiology, Ophthalmology, Cell Biology and Cell Therapy.
The introduction of information technology in the field of healthcare has provided improvements on numerous aspects , starting from digitization of patient data in electronic health records (EHR)  to providing clinical decision making 
As a result of the worldwide health care system digitalization trend,the produced healthcare data in 2011 have been estimated to be 150 Exabytes 150 * 10^18, and it is estimated to have 2314 Exabytes of newly produced data in 2020 [4,5]. However, processing these data efficiently so that useful information and new knowledge can be extracted remains a real challenge. In fact, the ever-increasing amount of collected data withstands the ability of current data analysis systems. As a result, healthcare systems are increasingly burdened. This is called the “Data Rich/Information Poor (DRIP)” syndrome . DRIP means that we are collecting more data than we can analyze. Fortunately, with the latest advancements in data analysis and decision-making systems, overcoming this challenge seems to finally be feasible.
From the standpoint of physicians reviewing the current stage of ML and AI in healthcare, the question is now are we ready to completely adopt the assistance of this technology in our profession? The answer is that there are still many limitations to fully incorporate these systems in the field of healthcare. These include the need for legal and ethical guidelines and frameworks that deal with critical cases. Multi-level training which would provide physicians with the proper background concerning this technology is imperative. And then there is the question of how to integrate it safely and compassionately into daily clinical practice. And what is the proper infrastructure to implement these systems? To add to this, financial burden is equally a matter to be considered, at least in the set-up phase . Looking to the positive, such systems would help physicians save time and effort and would assist physicians in the decision-making process. Given the enormous number of examples these systems are trained with, their observations exceed what any physician alone has witnessed throughout their career and can be of great benefit . AI alone or in partnership with ML seems to be an effective solution for enhancing the quality of personalized medicine and for accelerating the rhythm of evolution for complex diagnostic and therapeutic techniques such as in the field of genetics, small molecule, and super target therapies.
In our opinion, digital transformation in the service of medicine must be based both on clinical expertise—to guarantee maximum effectiveness—and on precise IT guidance in order to overcome limitations.