With any tool, technique, method or system they developed, humans lead to reorganization of the natural, spatial, temporal conditions which created and defined them. Let’s take AI, for example.
There is no field where AI does not interfere, interact, lead to change, or improve. Of course, one of the most important issues of our life is health. And the use of artificial intelligence in health has already started to transform this field.
Physicians have been performing analysis, diagnosis and treatment for hundreds of years. They accumulate and convey what they know and experience verbally and in writing. This is how medical science / art / profession has evolved and continues to evolve. Of course, medicine is not an isolated field, developments in the fields of biology, anatomy, physiology, etc. have led to the development of medicine. Moreover, the development of engineering disciplines, the development of many fields from genetics to imaging, from biomedical devices to hygienic issues have greatly contributed to the development of medicine and human health.
Especially, the amount of data growing day by day and the increase of analytical applications will contribute to the development of analysis, diagnosis and treatment methods. As the work done previously by human mind is done through the algorithm, error rates will decrease, sensitivity will increase and as a result, more lives can be saved, life expectancy will be longer, health quality will increase, health spending will decrease.
Especially if AI comes into play, human errors will decrease, sensitive diagnoses beyond the human mind will be made, the best treatments can be developed based on the data collected worldwide, even preventive measures can be taken based on the predictions, and recommendations and actions can be produced to eliminate diseases.
Medical Solutions Powered by AI
We can already talk about many medical solutions powered by AI. The first examples that come to mind are applications related to personal health assistance. One of them is ADA. Ada’s core system connects medical knowledge with intelligent technology to help all people actively manage their health and medical professionals to deliver effective care.
Another one is Apple’s iOS Health. This health app consolidates data from your iPhone, Apple Watch, and third-party apps you already use, so you can view all your progress in one convenient place. You can see your long-term trends, or dive into the daily details for a wide range of health metrics.
The use of artificial intelligence in medicine is no longer a myth. Now, the greatest assistant of doctors in every field are algorithms, machine learning systems and robots equipped with many abilities…
AI revolutionizes health as it does in every area of our lives. Health services worldwide are also significantly affected by this change. Machine learning and AI affect physicians, hospitals, and all other health-related areas.
According to Eric J. Topol’s article published in the journal Nature Medicine, everyone in the healthcare industry, from specialist doctors to first aiders, will use artificial intelligence technology in the near future.
According to GE’s projection, the artificial intelligence market for the health sector will exceed $ 6.5 billion by 2021. Considering that 39 percent of decision makers in the health sector plan to invest in machine learning and predictive analysis systems, this figure will increase further in the coming years.
How will AI Contribute to Our Health?
So, how will AI, ML and algorithms create changes in hospitals and contribute to our health?
We can say that the most benefited area is and will be the diagnosis of diseases. Accurate detection of diseases requires years of medical education. Diagnosing even after this training, is challenging and time- consuming. In many areas of medicine, the fact that the demand for specialists has exceeded the supply puts physicians in stress, and the diagnosis of diseases is further delayed.
Machine learning - especially deep learning - algorithms have made great progress in the automatic diagnosis of diseases recently, making the diagnostic process cheaper, easier, and more accessible.
Machine learning is useful in the following similar areas, where the diagnostic information examined by physicians is digitized:
– Lung cancer and stroke diagnosis by analyzing computed tomography scans
– Determination of the risk of sudden heart attack by analyzing electrocardiograms
– Classification of lesions by analyzing skin images
– Determination of diabetic retinopathy indicators by analyzing eye images
Thanks to the abundant data available in these areas, algorithms can be as successful as specialist physicians on the diagnosis. The only difference is that algorithms can diagnose in a very short time and can do this cost-effectively from anywhere in the world.
AI is especially popular in the field of Radiology. More than two billion chest X-rays are taken each year in the world. According to the researches, AI algorithms are more successful than people in evaluating these X-rays and diagnosing diseases. In addition to X-ray films, these algorithms are used in all kinds of medical imaging systems such as CT, MR, echocardiogram, and mammography, and results are obtained at speeds up to 150 times compared to humans.
According to studies, physicians spend much more time on data entry and desk work than they do actually talking to and engaging with patients. When processes like data entry and analysis of test results are automatedAI systems will alert and inform doctors about potential problems, enabling them to be more interested in patients and interpret signals more healthily. Considering that the world population is getting older and the need for a doctor is increasing, every second gained can lead to the survival and prolongation of many people.The question of whether AI or physicians are also on the popular side of the issue. In emerging countries such as China where there is an acute shortage of trained doctors, “Doctor vs. machine” competitions are very popular. This is illustrated by the Chinese TV broadcast of the brain tumour diagnosis and progression prediction competition between a team of 25 expert doctors against the Biomind artificial intelligence (AI) system. The 2:0 win of the AI over the humans in analyzing brain images gained high visibility in China.
AI-supported Surgery & Drug Development
Another area where artificial intelligence is used in medicine is surgery. AI systems can guide surgeons during the operation by analyzing patient data before surgery. Systems can also combine data on past surgeries and develop new and more effective surgical techniques. Researches show that complications are reduced by five times, and hospital stay is reduced by 21 percent in AI-supported operations.
Another field that uses artificial intelligence is drug development. Developing drugs is a very expensive process. The majority of analytical processes during drug development can be carried out much more effectively by machine learning. This will shorten years of work and reduce millions of dollars of investment.
AI is successfully used in all four basic stages of drug development:
– Determining the targets to be intervened
– Identifying potential drug candidates
– Acceleration of clinical trials
– Finding biomarkers for the diagnosis of the disease
AI-supported Personalized Treatment
The last area powered by AI on which I want to talk about is Personalized Treatment. Different patients react differently to medications and treatments. Therefore, personalized treatment is critical to prolonging patients’ lifespan. However, it is not easy to identify the factors used to determine which treatment method to choose.
In the article of Doctor Bertalan Meskó, who describes artificial intelligence as “the stethoscope of the 21st century,” it is stated that AI will make the “uniform” treatment history and suggest personalized treatments, therapies, and medications.
Machine learning can automate this complex statistical study and identify indicators that will be used to determine the patient’s response to a particular treatment. The system learns this by cross-evaluating similar patients by comparing the treatments and results applied to patients. The resulting predictions can make it easier for doctors to determine which treatment to apply.
For example, colorectal cancer patients in Brazil usually refuse the surgical removal of the colon because of cultural reasons. That’s why oncologists turn to methods such as radiotherapy and chemotherapy. However, only 20 percent of patients respond positively to these methods. So, how will it be determined which patient is in this 20 percent group? Here, deep learning algorithms come into play. Algorithms scan the data of patients and determine the appropriate treatment method in a short time and accurately.
AI and the Coronavirus
It is obvious that AI makes remarkable contributions to healthcare. And a question comes to mind since it is high on the agenda: What about coronavirus? Although the spread of the virus is a very recent development, AI-powered applications for virus diagnosis have already appeared. AI company Infervision launched a coronavirus AI solution that helps front-line healthcare workers detect and monitor the disease efficiently. Imaging departments in healthcare facilities are being taxed with the increased workload created by the virus. This solution improves CT diagnosis speed, they claim. Chinese e-commerce giant Alibaba also built an AI-powered diagnosis system. They claim it is 96% accurate at diagnosing the virus in seconds. Let’s hope that AI contributes to the development of an ultimate solution to stop the spread of the disease.
The global willingness to use artificial intelligence and robots is increasing.We can say that the main factor in this increase is the desire for faster, intuitive and low-cost health services. Trust in technology is critical for increased use and acceptance; however, ‘human relations’ remains a key component of the health care experience. So, it looks like we will be able to get the most effective results when we combine the power of AI with humans.
Publish Date: March 28, 2020
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