یادگیری ماشین و هوش مصنوعی در خدمات پزشکی
Abstract
Introduction
Basic definitions
Patient Intake: obtaining initial patient data
Radiology
Hematology
Neurology
Oncology
Cell biology and cell therapy
Cardiology
Ophthalmology
Conclusion
Acknowledgments
References
Abstract
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-tofollow 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.
Introduction
The introduction of information technology in the field of healthcare has provided improvements on numerous aspects [1], starting from digitization of patient data in electronic health records (EHR) [2] to providing clinical decision making [3]
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 [6]. 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.