ARTIFICIAL INTELLIGENCE IN HEALTH CARE


What is artificial intelligence (AI)?

ARTIFICIAL INTELLIGENCE IN HEALTH CARE by fetetch


There have been so many definitions of artificial intelligence (AI) over the last ten years, John McCarthy defines this in 2004. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
Artificial intelligence (AI) has the intention of achieving to imitate the human cerebral system. AI-driven programs are designs to make decisions with less human intervention, some wonder if AI will soon make the crucial decisions, we now entrust to our paramedics.

AI in health care’s purpose is mainly to refer doctors and hospitals with potentially life-saving information. This also includes treatment methods and their results, survival rates, locations, and innumerable and sometimes interconnected health conditions. New AI devices can detect and analyse major and minor changes in patients and make predictions through machine learning that’s designed to identify potential health outcomes.

ARTIFICIAL INTELLIGENCE IN HEALTH CAREs

In this article, we go through the current facilities of AI in healthcare, and we will focus on the AI future. Here we talk about the medical perspective of relevant aspects.


Motivation to launch AI in the healthcare sector

The benefits of AI been immensely discussed in the medical field. AI can use cosmopolitics design to ‘learn’ voluminous health data and use gathered insight to support clinical practice.

It can also be fitted out with learning and improvement potentiality to improve its accuracy based on feedback. An AI system can aid medical practitioners by showcasing up-to-date medical information from periodicals, textbooks, and clinical practices to make sure proper patient health care.

Furthermore, an AI system can help to reduce the chances of misdiagnoses and therapeutic difficulties that are inevitable by clinical practitioners. Besides, an AI program gathers helpful information from a large number of patients to formalize real-time inferences for health risk alert and health outcome predictions.


Data to be analysed by AI programs
In the new century of technology, computing allows us to use the data to benefit patients. An individual has hundreds of thousands of healthcare data points, if not millions. So, when you have data sets the data need to be collected appropriately and correctly for the power of machine learning.


Specially, in the diagnosis stage, a substantial proportion of the AI literature analyses data from diagnosis imaging, genetic testing and electrodiagnosis. For example, Jha and Topol urged radiologists to adopt AI technologies when analysing diagnostic images that contain vast data information. Li et al studied the uses of abnormal genetic expression in long non-coding RNAs to diagnose gastric cancer. Shin et al developed an electrodiagnosis support system for detecting neural injury spot.

⦁ Hardware used for AI
AI hardware is divided into two major categories. The first one includes machine learning (ML) techniques that analyse modified data such as imaging, genetics and EP data. In the medical applications, the ML procedures instructed to assemble patients’ traits, or conclude the prospect of the disease upshots.


The second category includes natural language processing (NLP) methods that gather the information from unmodified data such as clinical notes/medical journals to support and nurture structured information. The Natural Language Processing procedures works upon changing texts into machine-code data, which can then be analysed by Machine Learning techniques.

The following flowchart shows clinical data generation, through Natural language Processing data enrichment and Machine Learning data analysis, for paramedic’s decision making. We comment that the road map starts and ends with clinical activities. As powerful as AI techniques can be, they have to be motivated by clinical problems and be applied to assist clinical practice in the end.

IIII. Disease
Despite the increasingly high ration of AI literature in healthcare, the research only focusses on a few disease types: cancer, nervous system disease and cardiovascular disease
focus

The focus on only these three diseases is not completely out of blue thing. All three diseases are leading road to death; therefore, early diagnoses are crucial to prevent the deterioration of patients’ health status. Furthermore, early diagnoses can be potentially achieved through improving the analysis procedures on imaging, genetic, EP or EMR, which is the main strength of the Artificial Intelligence system.

⦁ Physical robots

Physical robots are working alongside with paramedic staff worldwide, as per reports 200,000 industrial robots are installed each year around the globe. They perform pre-defined tasks like delivering supplies in hospitals. And recently, robots have become more coworking with humans and are they easily trained for working on a desired task. They have become more intelligent now a days with the advancement of technology. AI capabilities are being embedded in their ‘brains’ (operating systems). By the passage of time, it seems like the improvement ratio in artificial intelligence growing day by day that we’ve seen in other aspects. AI would be incorporated into physical robots soon enough to surprise the population of world.


Surgical robots, initially approved in the service in USA in 2000, provide super natural helping hand to surgeons, improving their ability to see, create precise and minimally invasive incisions, stitch wounds and so on. Important decisions are still made by paramedic not so robotic surgeons. But daily routine surgical procedures using robotic intelligence include gynaecologic surgery, prostate surgery etc.

⦁ Patient engagement

Patient engagement have long been seen as the least one problem of healthcare – the last hurdle between ineffectiveness and good health results. The more patients proactively participate in their own well-being and care, the better the outcomes – utilisation, financial outcomes and member experience. These aspects are increasing day by day and being looked by volumized data and AI.

⦁ Abidance applications

Contributor paramedics and hospitals often use their medical experience to make a plan of treatment that they know will improve the chances of betterment of a chronic disease or an acute patient’s health. However, that often doesn’t matter if the patient fails to make the behavioural adjustment necessary, e.g., losing weight, scheduling a follow-up routine check-up, filling prescriptions or following completely with a treatment plan prescribed by doctor. Noncompliance – when a patient does not follow a course of treatment or take the prescribed drugs as recommended – is a major problem.

There is no doubt that artificial intelligence has played a major role in the field of medicine. AI insured the safety of many lives around the globe. With the enhancement of technology, the world has entered into a new era of treatments and cures.

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