Updated: Jul 17, 2020
The only source of knowledge is experience ~ Albert Einstein
Wouldn’t it be better if all the doctors were as smart as the top 20% of the doctors in the world?
The diagnostic mysteries and unnecessary deaths in hospitals would drastically decline. But in reality, that is not possible!
Advances in healthcare have immensely improved over time allowing doctors to diagnose and provide efficient treatment for diseases. The main agenda which differentiates the doctors is their approach and viewpoint to various patient problems and the type of health system that supports them. This is what causes such discrete variation in the clinical outcomes and it is the reason why artificial intelligence is the best solution out there to improve the doctor’s capabilities.
Artificial intelligence is an emerging field of computer science that consists of designing intelligent computer systems which exhibit the characteristics with human intelligence.
With the application of state-of-the-art machine learning algorithms, the healthcare industry can witness a massive boom in both medical advancements and business sectors, in the coming years. The basic premise behind machine learning is for the machines to be able to learn on their own through experience.
To think of a myriad of diseases where we can apply artificial intelligence, the central mission of the healthcare has not changed much in the past few hundred years, as it always been to provide the most useful diagnosis for the patients. But what has changed tremendously is the amount of data at disposal about the patient including genomic data, transcriptomic data, morphological data.
The AI in the healthcare market is expected to grow at a compound rate of 48% in the next five to ten years. The AI in health has a simple motive, to erase the factor of luck when it comes to patient care and diagnosis.
Recent Trends and Future Scope In Healthcare Across Globe
The year 2019 is witnessing a rise in healthcare chatbots. The national health service of Britain has already incorporated the health chatbot technology for a trial period of time. The main intention behind this was to reduce the burden for the medical emergency helplines.
The initial phase of the health chatbots resulted in patients not trusting the bots. But gradually, as these bots developed, the patients started embracing the chatbots. The bots are using the expert system technique in which there is a knowledge base where the dynamic content such as doctors knowledge, past experiences are stored and an inference system uses this knowledge to give and suggest recommendations to the users.
The bots have a tremendous application out of which, some are listed below:
Information retrieval about patient’s medical history
Implementing comprehensive dietary strategies for the patients
Treatment follow-ups and adhering to the provided schedule for the discharged patients.
Medication advise and recommendations
Clearing queries related to insurance and bill
The shift in Location of Healthcare Facilities
With the application of Artificial intelligence, the healthcare diagnostics facilities have changed over time. From the visitation to certified and limited healthcare facilities, AI can provide diagnosis anywhere; whether you are in your home, whether you are sitting in your car, or if you are traveling.
When it comes to the future of healthcare, cars could prove to be one of the most valuable diagnostic machines.
The researches and top-notch engineers in Mercedes have already developed cars which have incorporated the Computer Vision technology and by using various deep learning algorithms, they are able to tell if the has become too tired to drive. Moreover, they are aiming to arm their cars with more and more healthcare sensors. The seatbelts, the steering wheel or practically anything which we can touch, can potentially become a sensor to collect dynamic information. They are able to detect the drop in blood pressure, chances of heart attack, which are not only the improvements in the healthcare diagnosis but also a major leap in terms of security
Smart Alarm Technology
Are you fed up with waking up to the torturous alarm clock? If yes, then you should have a look the smart alarm technology. There are tons of wearables, sleep applications and sleep sensors available in the market. But the holy grail of healthcare is the smart sleep alarms. Our sleep consists of various sleep cycles. These cycles signify different stages of our sleep such as rested, groggy and fully rested. The smart alarms use artificial intelligence to analyze our sleeping patterns and help in waking us up at just the right time. This enhances our productivity and helps us get enough sleep.
Suppose you have tons of equipment in your organization and you want to keep records and use those equipment effectively. So what do you do?
You start writing it down on paper. Now tracking everything using paper-work is cumbersome. Hence Medical operations software come into play. It can be used from anywhere, be it your computer or mobile phone. It has some jam-packed features such as tracking of annual maintenance, equipment, Expenditure, Financial records, and solution recommendations. Its integration with Artificial Intelligence has enhanced its usage immensely.
With AI, a new term called the predictive analysis has been introduced. It uses state-of-the-art Artificial intelligence and machine learning algorithms to predict which equipments will go down in the future.
This has also helped us with various other features given below:
Artificial Intelligence provides predictive availability of the doctors in the hospitals by incorporating the machine learning algorithms which takes in input such as doctor’s health, age, behavior, salary, etc.
The prediction of medicines available in government hospitals by using machine learning algorithms.
Grouping similar patients based on the patient data for quick identification of their condition.
Improving the clinical effectiveness of patient satisfaction
Improving the quality of clinic and care by providing wellness prevention and disease management
Prediction and recommendation for improving financial and administrative performance
Medical Image Analysis
Is it possible for Artificial intelligence to detect and predict diseases based on a medical image or a body scan? Yes! Studies have shown that AI can not only predict and detect the diseases, but they can do so better than any doctor provided if the right kind of dataset is used to train the model. This, in turn, would save a huge amount of cost and help in efficient patient care.
The researchers have proposed that by 2020, the machine vision market will be worth $9.5 Billion. And out of this, typically 37% of the total share would be taken down by the medical image analysis. Moreover, this image analysis will not only be focused on hospitals or ultrasound, but it will be spread to a variety of modality.
The three main goals for the future of clinical imaging are:
Increasing the demand for medical imaging in local healthcare.
Decreasing the turnaround time which is usually caused by the massive data.
Diagnostic accuracy which eventually leads to the quantification of the medical images.
The main question that arises here is that in what major areas is the Artificial Intelligence already integrated into medical healthcare's daily workflow?
For this, let us consider an example of how AI is changing how the radiologists work. In today's world, there is advanced visualization software that assists the radiologists in the reading of medical images of X-rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. The AI-powered advanced software automatically segregates the lungs, heart, aorta and others.
Science is built on accumulated knowledge.
But with the advancements in machine learning, deep learning algorithms, AI can push our human capabilities to find the things that we never knew to look for, and we never knew that they even existed. Hence, the main aim of artificial intelligence is to effectively integrate this data to provide the best and the most useful diagnosis for the patient.