// Neural Networks
How Neural Networks can be Used in Health Industry: 10 Use Cases
// Neural Networks
The artificial intelligence market in healthcare has been estimated at $8.23 billion in 2020 and is expected to reach $194.4 billion by 2030, growing at an average of 38.1% from 2021 to 2030. The main driver of market growth will be artificial neural networks, because they are the reason how a digital "mind" can learn so quickly and efficiently. In this article, we will tell you exactly how artificial neural networks are used in medicine today.
Surgeon's procedures require a deep knowledge of medicine, a high level of accuracy and the ability to adapt quickly to changing circumstances along with constant attention over an extended period of time. Although well-trained surgeons typically have all of these qualities, but they are just ordinary people and therefore can make mistakes, especially if they have a busy schedule. According to research by Johns Hopkins University, surgical errors occur more than 4,000 times a year in the United States alone. For example, at least 39 times per week a surgeon leaves foreign objects inside patients, more than 20 times per week surgeons are performing the wrong surgery or operating on the wrong organ.
And these are not just words: the robotic surgery industry has already been valued at $40 billion and it's showing excellent results:
The medical community recognized long ago that imaging can greatly increase the probability of detecting disease. The problem is that even the trained human eye of a doctor is often unable to recognize the fine details in images. Besides, also we shouldn't forget about the problem with "blurred eyes": the researchers from Harvard University added the image of a gorilla to x-rays and demonstrated them to doctors - 83% of radiologists didn't notice the image of a gorilla on the x-rays.
Here are a few more examples of medical imaging with neural networks:
Virtual nurse assistants such as Care Angel are capable of conducting first appointments, arranging hospital visits, partially maintaining medical records, reporting test results and even conducting health checks using voice. Another example of such a solution is Sensely. This virtual assistant is an interactive application designed to streamline the medical workflow, automate routine processes and reduce the cost for monitoring patient care. Studies show that the use of Sensely can reduce labor costs for patient monitoring by 66% compared to the usual procedure.
The New England Journal of Medicine (NEJM) survey among British physicians found that more than 80% of respondents considered physician burnout to be a problem in their organization. At the same time, half of these respondents reported that "relieving administrative tasks" could fully or partially solve this problem by allowing physicians to share more time with patients as well as spending additional time on professional development. Moreover, the biggest complaint about the necessity of maintaining Electronic Health Records (EHRs) for patients, which are being used by many hospitals around the world.
One example of such solutions is Olive, an AI-powered platform that automates several administrative processes, such as verifying the eligibility of unwarranted medical claims, transmitting necessary medical data to appropriate health care providers, etc. Olive integrates easily with existing medical software tools.
K2 Process Automation provides similar services. This platform uses artificial intelligence to optimize the management of medical facilities and their personnel. Specifically, K2 AI can automatically update data in patient records, handle patient requests by sending them notifications when they receive test results, log calls and save information appropriately.
At the end of December 2021, the BlueDot platform predicted a coronavirus outbreak in China. BlueDot's artificial intelligence, which studied news in 65 languages, people's social media conversations and disease reports, reported the outbreak of a new flu-like infection a week earlier than the World Health Organization (WHO) had done. And this is not the first such case. BlueDot had previously predicted a Zika virus outbreak in Florida in 2016, six months before it happened. BlueDot had previously predicted the Ebola outbreak in 2014 and its spread outside of Africa.
Another startup that has shown its ability to predict infectious disease outbreaks is Metabiota. According to TechCrunch, the platform has helped to stop the spread of Ebola in Sierra Leone.
Atomwise, an American pharmaceutical company using artificial intelligence capabilities, in partnership with IBM, screened compounds capable of binding to a glycoprotein that prevents Ebola from entering cells inside the body during the 2015 Ebola virus outbreak in West Africa. The Atomwise neural network performed this analysis in less than a day, although this process usually takes months or even years.
This was the first time that AI had accelerated the drug development process so much. In another five years, the United States will approve the first drug almost entirely created by AI - the drug DSP-1181 for the treatment of obsessive-compulsive disorder. This drug was created at Exscientia and according to its management, it took only 12 months.
But that's not the limit, either. Insilico Medicin claims that their GENTRL AI is capable of creating new drugs against certain pathologies in just three weeks. It will take about 25 more days to select the best option and test it. Thus, it takes only 46 days to create drugs with GENTRL.
However, the most ambitious project in this direction is Deep Genomics. The company is developing an AI called Project Saturn to discover new methods for repairing the effects of genetic mutations. Deep Genomics has already estimated 69 billion oligonucleotide molecules compared to 1 million targets in silico to create a library of 1000 compounds that are experimentally validated to manipulate the cell biology on purpose.
Personalized medicine is another resource-intensive area in healthcare where artificial neural networks can be useful. This is because each treatment case is unique, as each person has a unique immune system, medical history and current state of health. And all of this has to be taken into account to maximize the effectiveness of the treatment. The only thing is that a doctor, firstly, cannot always see all the nuances in the patient's condition and secondly, the doctor does not have the necessary competence to make a comprehensive decision to build the most effective treatment plan taking into account all these nuances - this requires the deepest knowledge in all fields of medicine, which is unrealistic.
On the other hand, artificial intelligence can do it. It is able to study and analyze huge amounts of data and make decisions that consider all the available data on both the patient's condition and treatment options. Currently, there are not a lot of such projects (they are too complex and require huge amounts of high-quality medical data). But there are already the first successes in this direction: IBM's AI Merative (formerly called Watson Oncology) is designed precisely to solve such problems.
Fraud is another important problem in the health care industry. Counterfeit drugs, insurance fraud as well as substandard, illegal and deliberately ineffective medical services cause as much harm to the industry as fake and substandard clinical trials. In the United States alone, it is conservatively estimated that health care fraud costs the country about $68 billion a year or 3% of the nation's total health care spending.
Artificial intelligence based on neural networks can also combat this problem. For example, the medical company Aetna has about 350 neural network models for security and fraud protection. Highmark Inc also has its own NN to combat fraud and according to the company, their neural network-based product has already helped the company to save $245 million.
How often have you missed medication appointments, forgotten or ignored preventive exams with your doctor? Surveys show that this is a very common problem and doctors have almost no control over it. Moreover, admonitions and criticism of patients' behavior by doctors only exacerbate the problem because patients become ashamed of their illnesses and simply stop going to the hospital.
Different gadgets and apps that correct people's behavior can help with this. The most obvious example is fitness bracelets and smart watches, which help set goals (for example, to walk 4 kilometers a day) and track their achievement, as well as reminding about preventive check-ups and the importance of physical activity. More sophisticated apps, such as Somatix, track patients' daily activities and point out their habits and routines so they can focus on eliminating them and getting better.
Finally on our list, but not the least important problem is collecting and analyzing qualitative medical data. And it is not only collecting the patient's medical history, but also various related data from where the patient works and how often he goes to the gym until the movies he watches and the food he eats, which ideally should also be taken into account when constructing a treatment plan.
The problem is that it is very difficult and costly to collect this kind of data. People won't be willing to report on every purchase, run or horror movie watching. However, artificial intelligence-based platforms can do that. If such a solution is integrated with a smartphone, a smartwatch, a refrigerator, online banking and other sources, the artificial intelligence will collect such statistics for each patient - automatically and with privacy.
There are many examples of such solutions. Current Health, for example, has a gadget with artificial intelligence for medical monitoring of the human condition. This wearable device was one of the first to be approved by the U.S. Food and Drug Administration (FDA) for home usage. The gadget can measure a patient's pulse, breathing, oxygen saturation, temperature and mobility to provide this data to the doctor, who will make recommendations for the patient.