Artificial Intelligence (AI) has already proved a vital tool in the fight against Covid-19. Be it by the early detection of the disease by a Canadian start-up or in speeding up diagnostics more pragmatically and efficiently or via being an essential aid to healthcare workers by delivering food and medicine and sanitising cities. We have already seen an emergence of AI, that proved itself and hailed a permanent place on Mother Earth amongst once fear-mongering humans against the prevalence of automation.
The outbreak of Covid-19 has impacted the globe in such a ruthless manner that it didn’t give countries the time to channelize and measure the depth of the spread when it began. Crashing world economies, sweeping jobs, tumbling markets, and demolishing small, independent businesses.
The UN’s trade and development agency says the slowdown in the global economy caused by the coronavirus outbreak is likely to cost at least $1 trillion. This kind of loss takes a while to bounce back from, especially when it goes hand in hand with unpreparedness to tackle the repercussions of such a virus wave. There are many ways we, as a technologically developed race, could devise a plan for the next pandemic to avoid such an unprecedented situation from happening again.
1. Using big data as a driving force in early detection
Amongst many things that the Covid-19 has taught us and made us more aware about, one thing that we need to learn from it is advancing our tools in AI to help us prevent a second pandemic, or at least stop it from spreading at a rate it did in this blessed year of 2020. The first step of AI to help during the next pandemic is detecting it early on through the analysis of the right data. By monitoring the trends, the figures, the patterns from the ongoing virus breakout. The data from the severity of the lockdown rules, its implementation, and adherence to social distancing. To containing, the spread by multiplying the frequency of wearing masks as to not wearing them and all the available data from healthcare providers would prove essential for these machine learning algorithms to learn and get trained.
However, the biggest hurdle we face in this is the many types of unreliable data, such as the lack of data, incorrect data, language barriers, isolated data, incompatible means and tools and formatting, and integration issues. For example, the pharmaceutical industry has a very different way of storing data or rather the lack of it, with its constant updating for its traditionalist approach. Something like this could lead the AI algorithm to predict falsely due to its improper data deliverance.The Prejudice of Data Sets
AI offers the possibility to analyse, study, and detect at lightning speed as compared to having the same done manually. Hence, if organisations and data providers could fix the glitch of broken datasets, it could save the world from the emergence of the next disease early on.
2. AI and The Cure
Another vital area in which AI could pave the path in the future is to cure such diseases. Many AI-powered companies have set courses on their research and development of such drugs using it. This virus is especially taxing as its a single strand RNA structure, similar to other double-stranded infections, including HIV, Ebola, Influenza, and others. The Covid-19 mutates and has already mutated into 30 strains. The study found that different strains can generate vastly different levels of viral load than others. The South China Morning Post reports make them far more dangerous. Hence, making the cure and the vaccines currently in the works particularly hard to implement when they are ready.
Chinese tech firm, Baidu has made its Linear fold algorithm available to scientific and medical teams fighting the outbreak. The Linear fold algorithm, published in partnership with Oregon State University and the University of Rochester in 2019, is significantly faster than traditional RNA folding algorithms at predicting a virus’s secondary RNA structure. Analysing the secondary structural variations between homologous RNA virus sequences (such as bats and humans) can provide scientists with further insight into how viruses spread across species. Due to the recent outbreak, Baidu AI scientists have used this algorithm to predict the secondary structure prediction for the Covid-19 RNA sequence, reducing overall analysis time from 55 minutes to 27 seconds, meaning it is 120 times faster.
Iktos, a company specialised in (AI) for novel drug design headquartered in California, not long ago declared that the companies have entered into a collaboration agreement designed to hasten discovery and development of novel antiviral therapies. They will be using a fully automated end-to-end synthetic chemistry system to develop novel compounds and accelerate the identification of drug candidates to treat multiple viruses, including influenza and the coronavirus (COVID-19).
Google’s DeepMind is putting its artificial intelligence systems to a new task: trying to figure out specific properties of the novel coronavirus, which has killed thousands in the past couple of months. Recently, DeepMind, said it had put its Alpha Fold system to create “structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19.” These predictions are not verified, but they may help scientists understand how the coronavirus functions. In turn, it may be of use when developing a vaccine or cure. AI-based tools such as these by DeepMind, help in exploring the nuances of how the virus functions and how, conceivably, it can be neutralised.
3. Computerized Health Care Accessibility
The Israel-based Med tech company, Nanox, has developed a mobile digital X-ray system that uses AI cloud-based software to diagnose infections and help prevent epidemic outbreaks. AI can diagnose COVID-19 from CT scans, researchers in China claim. At least two teams have released studies that prove that deep learning can analyse radiological features for precise COVID-19 diagnoses faster than current blood tests, saving critical time for disease control.
Alibaba Cloud, the cloud computing arm of China’s internet giant Alibaba, is sharing its AI-powered disease diagnostic technology for free use by hospitals worldwide. Their Natural language processing model was used in the text analysis of medical records and epidemiological investigation by CDCs in different cities in China for fighting against COVID-19. Natural language processing (NLP) is a subcategory of AI that allows researchers to identify patients who are eligible to participate in clinical trials. NLP relies on algorithms that analyse medical reports written by doctors to identify the right people for clinical trials. With the amount of Covid-19 literature available, it makes the medical and research communities hard to manage the growing amount of data. NLP is a strong ally in confronting these untameable changes and updates in data and analysing it for better research. For example, Topic Modelling, a Natural Language Processing technique that uncovers the hidden topics in a collection of documents. Which topics are covered within the literature? It helps in segregating and decoding the motive behind it.
4. Robots- The Future Nurses?
We have seen the crippling numbers of nurses during this pandemic. The quickening cases left the governments with no choice but to call back the retired nurses back to the field and get the nursing students to enter the frontline to help fight the rocketing cases. Overwhelmed healthcare is the biggest issue many countries are facing, be it the lack of ventilators, lack of beds, or, most importantly, the lack of a medical professional. Many hospitals in countries like the United States, China, Thailand, Israel, Singapore, and India are already using Robots increasingly.
Meals and medicines were served; temperatures were taken and, also communications were handled by machines, one of them named Cloud Ginger by its maker CloudMinds, which has operations in Beijing and California. Patients in hospitals in Thailand, Israel, and elsewhere meet with robots for consultations done by doctors via video conference. Some consultation robots even tend to the classic checkup task of listening to patients’ lungs as they breathe. The Robot nurse called’ ‘Dokat Aura’ runs along photosensitive strips on the ground while in the Indian state of Tamil Nadu , Optical or magnetic sensors know precisely where to stop in the wards and return to the base after the work is done. It’s inbuilt cameras help her aka the robot monitor mildly infected patient’s dosage and food intake. Dotworld Technologies, based in India, the creator of these robots, plans to make 100 robots in 45 days and then around 100 robots a month. These battery-operated robots can be controlled with a remote and operated from a safe distance.
Innovative blood-drawing robots like Veebot or vein scanners such as VeinViewer or AccuVein could offer some help. In the case of blood drawing robots, these skilful machines reduce the whole process to about a minute, and tests show that they can correctly identify the best vein with approximately 83% accuracy. An example from a Stanford paper highlighted, ‘For instance, if a robot is programmed to remind its patients to take their medicine, it needs to know what to do if the patient refuses. On the one hand, denying the drug will harm the patient. On the other hand, the patient may get a refusal for several legitimate reasons that the robot may not be aware of. For instance, if the patient feels ill after taking medicine, insisting on administering the medication may be harmful. Leaving a reminder and ignoring any human response is also impractical because the robot will be replacing a human nurse, whose job is to make sure the patient is receiving proper care.
Moreover, what if the patient agreed to take medicine and then forgot? Should the robot stay and monitor the patient until the medication is received, or is that a violation of privacy? When and how should the robot inform the doctor if anything goes wrong?’
Robots helping in these cumbersome tasks of the ambiguous circumstances is an excellent relief for health care professionals who can then focus on patients who need urgent aid. However, Robots cannot cover the ethical aspects. Primary responses are automated and usually limited to a ‘Yes’ or ‘No’ regardless of the information they are trained with, making them hard to operate without being monitored.
Although nursing Robots are far-fetched to replace a human completely, Japan has already integrated them into their nursing sector. That is not only due to their precarious and unmatchable technological know-how only. Most of the Japanese have had an innate belief in the practice of the Shinto religion, which includes faith in animism, i.e., attribution of a spirit to objects. They are more likely to accept robots as an assistant in different spheres of the society than many of their western counterparts.
There really won’t come a time soon where the Robots could replace nurses completely, due to the high risk of errors and constant upgrading required, but they certainly could be an added asset in the fight against such life-threatening diseases.