2020 is a year that will go down in the annals of history as a landmark one. SARS COV-2 or COVID19 dominated headlines, and our lives for the better part of this year. The novel coronavirus has so far impacted 77 million people worldwide, and has claimed the lives of 1.7 million. Previous pandemics like the Spanish Flu in 1918 and The Plague in 1347 were actually far bigger in impact – in four waves, the Spanish Flu claimed nearly 100 milion lives while the The Plague or Black Death killed around 200 million – making these two pandemics among the most deadliest outbreaks known to man. More recently, the world has seen other pandemics such as tuberculosis and smallpox, as well as highly infectious diseases like SARS, Ebola and Zika have impacted Asian, African and certain Western nations. Although, COVID19 is not as deadly as the Spanish Flu, the Plague or even Ebola, it has spread rapidly across the world in just nine months. After impressive and rapid developments in vaccines to counter the effects of the virus, it appears that a new and potent strain has emerged in UK and may have spread to other countries in Europe.

But what is different about this outbreak compared to all the others is the role played by AI. In fact, COVID19 may be the first disease to be detected by an AI – a Canadian health monitoring platform called BlueDot used an AI-driven algorithm to search news reports, blogs, animal and plant disease networks and airline ticketing data to correctly foretell the origin of the outbreak in Wuhan, China as early as December 31, 2019. The World Health Organization and US Centre for Disease Control issued formal announcements nearly 10-15 days after.

In just a year, Artificial Intelligence has become a ‘nice to have’ technology to a ‘must have’ technology for businesses today. From healthcare, governance, retail and transport, the use of AI this year has expanded exponentially. Specifically, the application of AI in healthcare and lifesciences has been nothing short of extraordinary this year. Popular applications include real time disease monitoring, drug discovery, rapid analysis of chest x-rays and CT scans, protein synethesis, prediction of infection rate and related trends and pathogen analysis.

Early on, countries that suffered high numbers of fatalities were Italy, Spain and Iran. It became evident that the disease was adversely affecting a patient’s lungs, and those with comorbidities like diabetes, hypertension and cancer were more susceptible to contracting COVID19. As hospitals swelled with patients, medical staff were under pressure to treat them effectively. Specifically, they struggled with reading chest x-rays of affected patients – and this is where a discernible use of AI was found. Mumbai and San Francisco-based startup Qure.ai has successfully utilised AI to detect tuberculosis by screening x-rays, CT scans and MRIs. Extensive deep learning technology helped faster diagnosis and treatment. Following the COVID19 outbreak, the Qure.ai team developed a solution to detect lung anomalies caused by the virus, called qXR. A CE-certified product, qXR can interpret chest X-Rays in less than a minute, and detect findings such as ground glass opacities and consolidation indicative of COVID19, localise lesions and indicate if these lesions are bilateral and in which zones, detect the presence of cavities, nodules, pleural effusions, fibrosis and lymphadenopathy. This has greatly helped healthcare workers in countries like Italy, India, Mexico and Oman among others. Similarly, Bangalore-based radiology company 5C Network made the case for AI-guided radiology to play an active role in diagnosing COVID19 patients. It’s proprietary solution reads scans from hospitals, extracts data and provides a rapid analysis to the specialist via the cloud; effectively addressing the talent shortage of radiologists which is rampant not only in India but even in developed countries.

What would have taken years to expedite with regulatory approvals has now happened in months, and industry watchers believe 2020 could have become the watershed year for AI in healthcare. As hospitals and care providers sift through the sea of solution providers to single out the competent ones to work with, the trend this year has definitely paved the way for AI-driven healthcare applications to come to the market. According to Rajashree Damle, Vice President and Global Head of Portfolio – Digital Engineering and Manufacturing Services at Capgemini, COVID19 accelerated the digitisation of healthcare, and has given way to AI playing a pivotal role in precision healthcare and disease management in developing nations like India.

This year also turned the spotlight on the medical research fraternity and pharma majors to come up with a cure to coronavirus. Companies like Pfizer, AstraZeneca, Moderna among others have been toiling away since the start of the pandemic to better understand this novel strain and devise ways to mitigate mutation and spread. This is also an area where machine learning has played a significant role. To date, pharma majors have been extensively utilising AI and its various subsets to drive drug discovery – Pfizer has been using IBM Watson to improve its search for immuno-oncology drugs, while Sanofi signed up with Exscientia’s AI platform to look for metabolic-disease therapies. Drug discovery is a predominantly capital and time-intensive exercise, involving multiple trials with limited success, pharma companies are increasingly relying on AI and biospecific modules to make this journey a lot less expensive and more effective. The advancements made in this field so far have greatly helped the medical fraternity find drugs to combat the effects of COVID19. Remdesivir, in combination with Ritonavir and Lopinavir is one such example. A platform called IDentif.AI was able to deduce that a combination of the three drugs to combat the effects of COVID19, following the implementation on a 12-drug candidate therapy search set with 53,000 possible drug combinations.

Protein folding –determining the three-dimensional shape of a protein - is one of biology’s most vexing challenges. And Alphabet’s DeepMind successfully completed initial strides in protein folding in November using AI, and has released structural predictions of the proteins associated with COVID19. While these predictions are not validated yet by scientists, there is hope these findings will further galvanise the scientific community’s efforts to understand the virus better, and this could lead to breakthroughs in drug discovery.

This has been one of the toughest years known to us, and its been harder on some than others. But this has also been a breakthrough period for AI, and we’re privy to innovations that might have taken years to come to the fore. As we bid goodbye to 2020, there is hope that technologies like AI will help bridge existing gaps in healthcare even in 2021 and beyond.