The existing healthcare systems of our planet are among the most essential public services. The pandemic ridden world demands a health care system able to handle large numbers of patients with more efficiency. Remote diagnosis and patient profiling are also gaining popularity as in-person visits are becoming more and more injurious. Machine learning and AI is excelling in the health industry as they are known for reducing human errors. Despite all the ethical constraints, health professionals from all across the world are welcoming the idea of automation in the health industry. Though the degree of supervision this new paradigm requires is still an open question. This article will discuss the applications of Artificial intelligence and machine learning in health services. And try to penetrate further deep into the ethical constraints in order to understand the possibility of such implementations getting accepted.
Smart wearable devices
The sole purpose of smart wearable devices is to create an opportunity for remote diagnosis and monitoring. Obviously, these devices are able to record vitals like temperature and pressure if programmed accordingly and transmit them to the concerned health institute. Thus an irregularity in the vitals can help the institute predict the future health of their patient. And issue the necessary warnings.
For obvious diseases and symptoms, automated prescriptions can be deployed. The artificial intelligence in potential control of this aspect must be trained rigorously over a long period of time and must deploy in the cases of millions of subjects before actual deployment. An efficiently trained AI can trace the symptoms and figure out the potential set of drugs and under the supervision of a doctor, it can dispatch the same directly to the patient.
The presence of machine learning tools and AI makes the process of patient profile maintenance easy. As there is little to no chance of human error the profiling done by the tools are top-notch almost free of error. With profiling, the history of the patient can be accessed while designing therapeutic approaches for treatment.
Automated diagnosis by image recognition
Histology plays a major part in diagnosis as well as colourimetric assays, these diagnostics methods are perhaps the most suitable candidates for automation. Image recognition is computer vision and has made significant progress over the years. It is revolutionizing traffic management on city streets and is poised to introduce the revolution in healthcare. The opportunity is huge, and in order to grab that, one must work harder while the competition makes a decision. It is wise to browse the internet and find suitable AI ML courses in order to be ready for the right moment. Given the present state of the healthcare industry and the immense pressure, it is dealing with the call of duty might come sooner. However, before devoting to the service of humanity adequate training is essential. Any incompetence might result in the loss of human lives. It is thus wise to receive hands-on training while gaining knowledge.