The healthcare sector has a predominant role in the life of every individual. The human lifestyle has drastically changed which in turn increased the vulnerability to diseases.
More people seek treatment and the number of hospitals is growing day by day. It is a sad fact that people frequently turn unhealthy but it is equally important that whether they are treated thoroughly.
Thus it has become necessary for healthcare to turn smarter. Numerous advancements have been introduced to the medical facilities throughout these years which makes the diagnosis and treatment easier and more accurate.
The influence of Artificial intelligence is predominant among them.
The Healthcare system is enhanced with a diverse set of intelligent techniques that unbelievably enhance the quality of diagnosis as well as treatment.
These models are found to outperform human skills in many cases. The experts state that AI can replace humans in healthcare and it is assured that AI can provide high-quality assistance for humans in healthcare activities as well.
AI efficiently takes part in disease diagnosis, treatment protocol, medicine recommendation, patient monitoring, and drug development as well.
The long suit of the medical sector is that it has been generating a huge amount of data throughout time.
Correspondingly, the successful working of AI algorithms relies on the amount of data for which it is trained.
This results in a substantial hookup between AI and healthcare which in turn came out successful.
Big data in the healthcare industry is widely exploited by many companies to design and generate intelligent medical equipment.
AI has already taken part in every prominent department of healthcare in process of diagnosis as well as treatments.
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The first glint
The researches on the efficient utilization of AI in healthcare is now at heaps and is growing endlessly. The clinical decision support systems are the major users of intelligent systems since AI has powerful decision-making ability when trained with immense data.
The major focus lands on the diagnosis part where AI can derive proper inferences from the input data. This smart move was first made in the 1960s when the problem-solving program entitled Dendral was introduced to assist chemists to identify organic molecules in medicines.
As the tech in need
The demand for smarter versions of healthcare systems is being increased in this pandemic situation since the human to human interactions is insecure.
The intelligent healthcare facilities enable the continuous monitoring of patients and even facilitate the periodic distribution of medicines thereby reducing the risk factor of spreading the disease through medical staff.
Thus the use of intelligent healthcare is providently witnessed and experienced by the public and prompted numerous companies to raise funds for the enhancement of healthcare systems through artificial intelligence.
In context to these prevailing necessities, the United Nations is expected to invest $2 billion for the researches related to the development of smarter Healthcare Systems using Artificial Intelligence for 5 years.
Alzheimer’s Disease is no more challenging
Alzheimer’s Disease(AD) is one of the most challenging malignancies in the medical field due to the fact that it is incurable.
AD is a progressive memory disorder that initiates with memory loss which will gradually progress to death.
The anatomical reason behind memory loss is the slow decay of brain cells. Memory loss then follows with the disabilities of other organs of the body and finally ends in death.
The researchers are still trying to find a medication for curing AD but no ways are lit till now.
AD is a genetically transferable disease. The future generations of an AD diagnosed person have more chances to be diagnosed with AD.
Even though it is incurable, the earlier diagnosis is highly recommended to slow down the process of neuron damage.
Numerous studies had been carried out in artificial intelligence and deep learning to find the solutions for this confront. Most of the studies are based on the earlier and enhanced diagnosis of AD. A major set of studies are based on the scan images of the brain.
The earlier diagnosis of AD within its five stages using deep learning methods was prosed in a research document in which the MRI images are collected in large numbers to train the deep learning networks.
The five classes of AD dataset indicate the five stages of AD which spans from the normal stage to the diseased stage. The classification performance in the research paper was very good which signifies that AD can be detected in the very early stage using deep learning networks.
Diagnosis made precise and easeful
Several types of AI methods like machine learning, support vector machine(SVM), deep learning, etc have been used for different types of disease diagnosis and screening.
These techniques are proved to classify and sort out cardiovascular diseases and diabatic profiles from normal individuals using medical records.
A paper from the journal named Annals of Oncology, in 2018 has discussed the better detection of skin cancer using deep learning techniques, specifically by a deep convolutional neural network.
The model outperformed with 95% accuracy of detecting skin cancer from the images while that of humans was 86.6%.
The same fashion was found in the case of breast cancer detection in 2020 using google’s deep mind algorithm when the artificial neural network surpassed the human experts.
A huge collection of electronic health records were processed using machine learning classifiers to aid the doctors in patient diagnosis.
Artificial intelligence is likewise used in numerous cases of diagnosis and screening of patients which turned out to be extremely helpful for doctors to improve precision and accuracy.
The researchers are still active in this field since the medical sector takes the advantage of better disease diagnosis as well as effective handling of the huge collection of health records.
Intelligent and flawless care for patients
The number of patients has drastically increased in this pandemic situation by which the existing group of medical staff was insufficient to handle the situation.
The situation was then resolved by introducing a home quarantine strategy wherein the patients were isolated in their houses and the medical officials frequently monitored them to provide proper medication and necessary care.
This idea of remote care for patients was termed telemedicine which was then enhanced by the AI techniques that enabled the patient monitoring with the frequently collected data from sensors.
This not only reduced the hardship of frequent medical vitals but also improved the quality of patient care even from remote locations. The facility was more effective to care older population.
Smarter management and utilization of Electronic Health Records
The outdated health records were a burden for the hospitals and clinics and the effective handling of it was a hectic task.
The non-digital records required a huge physical space whereas the digital records required large memory space.
These were stored as dead assets until the AI started making use of the digital health records for a wide range of fruitful applications.
The scan images are the most demanded data for AI techniques since most of these networks can be easily and effectively trained using images.
The other records on medical vitals are also well utilized for deep learning and machine learning models to study patient behavior for the prescribed medicine, continuous patient monitoring, and telemedicine.
The heaps of dead records were thereby converted into the most useful input for enhanced healthcare in the future.
Predictions on drug interactions and the creation of new drugs
The right medicine for the right disease is the fundamental element of treatment. When it is important to provide proper medication, the availability of good quality medicine is also a critical case of thought.
If the patient is a victim of more than one disease, the case multiple medications should be handled with utmost care such that the drug-drug interactions are reduced.
The AI techniques are proven to handle this challenging task with good performance. The DDIExtraction Challenge conducted in 2013 cumulated the investigations on drug-drug interactions studied by a group of researchers of Carlos III University.
The machine learning algorithms were used to extract the effects of interacting drugs which helped the physicians to create medical prescriptions carefully.
Innovations on smarter skincare
The increased number of skin diseases is the aftereffect of atmospheric pollution. The treatment for skin diseases is usually difficult in procedures since minute information has to be inferred from the medical images to conclude on the exact disease and its causes.
This demanding task is simplified by AI-driven methods since dermatology is an image-abundant specialty.
When skin cancer detection using face images was proposed in a research paper authored by Han et.al, the classification of skin cancer was carried out from lesion images from the research work proposed by Esteva et. al.
Both the works are carried out using deep convolutional neural networks in which the malignant sample is classified out with a prediction of probability being malignant.
Noyan et al experimented with the convolutional neural network for the classification of skin cells from the microscopic Tzanck smear images which performed with an accuracy of 94% in classification.
Healthcare is thus a highly emerging sector in terms of smarter technologies powered by artificial intelligence since it has an abundance of data.
It is expected to get smarter in the upcoming ages thereby assuring the most easiest and precise diagnosis and thus assuring high-quality treatment.
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