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As long as people will be interested in health improvement there will be a place for medical technologies. Today we have access to more health data than ever. With AI we can process large amounts of information and gain insights that were impossible before. AI systems can contain infinitely more usable data than a human brain, which means that an AI will help us analyze information, recognize patterns, and evolve trends, identify long-term health dangers and make medical diagnoses in faster and more accurate ways. According to the forecast, the global market size for AI in healthcare is about to reach $28 billion in 2025. For instance, 1 in 5 AI startups operates in the healthcare sector in France alone.
The term “artificial intelligence” was first announced by John McCarthy at a conference at Dartmouth College in 1956. Until 1974, AI was aimed to solve problems in geometry and algebra and communicating in natural language.
Today, AI has many applications in different industries, including education, finance, and healthcare. AI has been applied to object, face, speech, and handwriting recognition; virtual reality and image processing, natural language processing, chatbots and translation, email spam filtering, etc.
The potential of AI is indisputable. Governments around the world are collaborating to ensure responsible development and use of AI. An example is the Global Partnership on Artificial Intelligence (GPAI), collectively created by 14 governments and the European Union in 2020.
In the healthcare industry, in particular, if you can create machines that do certain tasks precisely it can make a big impact. Radiologists can look at over 50 images a day while an algorithm can be trained on millions in hours. Also, it can go through more data in some period than a physician can in their entire life. AI is the key to prediction-making and decision-making. After the machine is being programmed, it will carry out tasks on its own, without specific human help.
One of the most common forms of Artificial Intelligence used in the healthcare industry is Machine Learning. ML uses algorithms and numerous techniques to teach computers to act on data. It learns from an amount of information, understands specific data patterns, and builds predictions based on these patterns. Machine Learning is powered by Artificial Intelligence and makes a computer automatic in its experience improvement.
Now let’s take a look at examples of AI technology in healthcare.
Machine learning can detect diseases at early stages and provide exact predictions about the effect of specific treatments. Using an algorithmic approach, AI can make valuable decisions and predictions that are improved with experience. It also helps in scenarios when you are living far away from the nearest doctor, has no time for a visit, or are uncertain if you have enough money to cover the cost of that visit. In this case, an option to receive a diagnosis through your smartphone may be lifesaving. Here are a few examples we want to share with you.
Researchers at the University of Nottingham have developed an AI that can successfully predict cardiac events better than medical experts. Stanford Thrun Lab graduates created an algorithm that can recognize and separate malignant carcinoma (a deadly form of skin cancer) from benign seborrheic keratosis (harmless wart-like growth).
The most complex forms of machine learning involve deep learning. A common appliance of deep learning in healthcare is the recognition of potentially cancerous bruises in radiology images. This technology is mostly applied to radionics or the detection of clinically relevant features in imaging data beyond what can be detected by the human eye. Both radionics and deep learning are most commonly used in oncology-oriented image analysis.
Medical error is the third leading cause of death in the USA. Thus, the benefit of integrating technology is in reduction if not complete elimination of fatal mistakes. AI can help professionals make the right predictions, particularly regarding the treatment of patients where most human errors occur.
The team of researchers at Duke University School of Medicine surveyed on the topic of the role of ai in healthcare, to be specific, in mental health. In research, they measured opinions about the possibility of artificial intelligence and machine learning to fully replace the average psychiatrist in performing key tasks: mental status exam, suicidality assessment, treatment planning carried out in mental health care, etc.
Survey respondents were 791 psychiatrists from 22 countries. 3.8% of respondents felt it was likely that future technology would make their jobs out-of-date. 17% express the opinion that future AI was likely to replace a human clinician for providing empathetic care. Most respondents (83%) felt it unlikely that future technology would ever be able to provide empathic care better than the average psychiatrist. A majority predicted that AI could fully replace human psychiatrists in two tasks: documenting and updating medical records (75%) and synthesizing information (54%)
The assumption of AI playing a role in mental health is reasonable. Smartphones offer people the ability to monitor themselves and to benefit from the way deep learning can analyze the data. So, we think artificial intelligence may become a disruptive force in psychiatry.
AI could develop new drugs faster than a team of scientists. Approximate estimates are 12 years and $2.9 billion for an experimental drug from concept to market. Take into account the time and resources invested in finding specialists, addressing unexpected side effects in clinical trials, and the chain of multiple errors. But with AI these numbers can be significantly decreased. As proof, there are few examples of such development. Pharma startup Insilico Medicine with researchers at the University of Toronto identified a potential new drug in 46 days. In 2017 US startup Atomwise has built a system it calls AtomNet to generate potential drugs for diseases like Ebola and multiple sclerosis.
The purpose of this technology is to help healthcare workers and reduce the pressure on non-emergency helplines. Also, it allows patients to get 24/7 assistance with basic medical questions. As for AI, World Health Organization introduced a new chatbot-like feature that is based on an Artificial Intelligence algorithm to help people quit smoking. Probably the chatbot could also help users to find a way to stop bad lifestyle habits in general. WHO partnered with Soul Machines, Amazon Web Services, and Google Cloud to develop a bot, Florence, to support tobacco cessation. Florence is a 24/7 virtual health worker able to provide digital counseling services to those trying to quit tobacco. The Bot also shares WHO public health messages and recommendations on tobacco and COVID-19.
Smart devices have been a useful tool in adult care delivery. Apple has developed smart wearable biometric trackers that are now available to thousands of patients around the globe. With AI technology, the functionalities of smart devices are broadened. For instance, now you can purchase a portable ultrasound device. With this device, you can do an ultrasound examination with the help of your smartphone. Despite having no experience in doing sound examinations, because the device is AI guided, you will be provided with all the necessary and applicable information
According to the Director of The Medical Futurist Institute, Dr. Bertalan Mesko telemedicine will evolve as a trend. Remote assistance through different devices will be a safe approach in an increasing number of countries. It will also cover more rural areas and video consultations will be added to care options. Telehealth will become a more common tool within medical care. Even in emergency rooms, hospitals, and insurance offices.
Artificial Intelligence is the competence of a computer program to perform tasks or reasoning processes that we usually associate with the skills of human beings. AI won’t replace medical professionals any time soon. The human factor is still very important for patients. But these technologies will lead to better care outcomes and improve the productivity of care delivery, eliminating errors and giving healthcare providers more opportunities to set the right diagnoses. It will also improve the day-to-day life of healthcare practitioners, letting them spend more time taking care of patients