Today, the life sciences industry is at a critical inflection point. Its public profile has elevated due to its success at quickly developing vaccines to combat the COVID-19 pandemic. It has also built up a lot of trust. Despite the persistent issue of vaccine hesitancy, health — including life sciences — rose up in the rankings to become the second most trusted sector after technology, according to the 2021 Edelman Trust Barometer.[1] 
 
While the life sciences industry rightly has the approval and trust of its stakeholders — including heath companies, insurers, clinicians and patients — such approbation gives rise to an important challenge going forward. This challenge is meeting those stakeholders’ ever-rising expectations.
 
The rapid development and mass deployment of COVID-19 vaccines, including the pioneering mRNA vaccines, highlighted to stakeholders what the industry is capable of achieving. At the same time, new technological advances are opening up the possibility of the life sciences industry making other breakthroughs that will transform the health experiences of patients, while potentially saving millions of lives.
 
Artificial intelligence- and machine learning-enabled transformation
 
With the maturation and advancement of artificial intelligence (AI), it is set to have a measurable impact on the life sciences industry. AI is enabled by complex algorithms that are designed to make decisions and solve problems. In combination with machine learning (ML) and natural language processing, which make it possible for the algorithms to learn from experiences, AI and ML will help life sciences companies develop treatments faster and more efficiently in the future, reducing the costs of health care, while making it more accessible to patients.
 
We already know that AI and ML have the potential to transform the following processes in life sciences:
 
Drug development. Thanks to its ability to process and interpret large data sets, AI and ML can be deployed to design the right structure for drugs and make predictions around bioactivity, toxicity and physicochemical properties. Not only will this input speed up the drug development process, but it will help to ensure that the drugs deliver the optimal therapeutic response when they are administered to patients.
 
Diagnostics. AI and ML are effective at identifying characteristics in images that cannot be perceived by the human brain. As a result, it can play a vital role in diagnosing cancer. Research by the National Cancer Institute in the US suggests that AI can be used to improve screening for cervical and prostate cancer and identify specific gene mutations from tumor pathology images. There are already several commercial applications in the market. Going forward, AI may also be used to diagnose other conditions, including heart disease and diabetic retinopathy. By enabling early detection of life-threatening diseases, AI will help people enjoy longer, healthier lives.
 
Clinical trials. The fashion in which clinical trials have been designed and conducted have not materially changed over the last decades, until the pandemic brought about necessary change to help transform some components of the clinical trial process, such as study monitoring and patient enrollment. As the research and development cost comprises 17% of total pharma revenue and has increased from 14% over the last 10 years,[2] there are calls for long overdue decentralization to be brought about by technology. Some commercially available platforms have made this concept a reality.
 
Supply chain. By analyzing longitudinal data, AI and ML can identify systemic issues in the pharmaceutical manufacturing process, highlight production bottlenecks, predict completion times for corrective actions, reduce the length of the batch disposition cycle and investigate customer complaints. It can also monitor in-line manufacturing processes to ensure the safety and quality of drugs. These interventions will give life sciences companies confidence that their manufacturing processes are operating at a high standard and not putting the organization in breach of regulations. Importantly, the bottlenecks caused by the pandemic tested the resiliency of the entire supply chain ecosystem. Furthermore, life sciences companies can improve their efficiency by applying AI to their supply chain management and logistics processes, aligning production with demand and with an AI-enabled sales and operations planning process.
 
Commercial and regulatory processes. Reviewing promotional content for compliance purposes has been a necessary, yet constricting, stage gate for any biopharma company. The current medical, legal and regulatory review processes for approving product marketing materials are painfully slow and can be inconsistent, leading to repetitive cycle times. Promotional content is the single most important source of information of newly approved products, given the paucity of peer review literature at launch. This holds back approved medications from reaching providers and patients sooner. Now, AI and ML have been proven to be utilized to significantly reduce the medical, legal and regulatory review time, while improving the accuracy of the content. This will improve the speed and reliability of the processes, enabling therapies to get to market quicker.
 
Beginning of a new digital era with broader utilization of AI and ML
 
We are only in the early stages of deploying AI and ML in life sciences. And while we can already see their promise, the industry is likely to find numerous future use cases for the technology that we cannot even begin to conceive of today. There already are early signs as to how AI can be incorporated into surgical robots, with the theory that AI-powered surgical robots may one day be allowed to operate independently of human control. Whether that ever happens is likely to depend on regulatory frameworks and legal liabilities, rather than technological advances.
 
Inevitably, there will be a massive amount of change as we move past the current inflection point. The proliferating variants of the severe acute respiratory syndrome coronavirus, such as Omicron, and the successful deployment of mRNA technology leading to rapid development of the COVID-19 vaccines are putting pressure on the life sciences industry to do more — and faster — when it comes to developing and manufacturing treatments for cancers and other diseases. So how can it rise to this challenge? To meet the expectations of its stakeholders, the life sciences industry will undoubtedly need to exploit the full potential of AI and ML.
 
[1] Kristy Graham, “Science and Public Health: Transparency is the Road to Trust,” Daniel J. Edelman Holdings website, https://www.edelman.com/trust/2021-trust-barometer/insights/science-public-health#top, accessed December 2021.
 
[2] Capital IQ report about top 25 biopharma companies, 2021.
 
Arda Ural, PhD, is the EY Americas Industry Markets leader for EY’s Health Sciences and Wellness Practice.  Arda has nearly 30 years’ experience in pharma, biotech and medtech, including general management, new product development, corporate strategy and M&A. Prior to joining EY, he was a Managing Director at a strategy consulting firm and worked as a VP of Strategic Marketing and a BU Lead at a medtech company. Arda holds a PhD in General Management and Finance and an MBA from Marmara University in Istanbul, as well as an MSc and BSc in Mechanical Engineering from Boğaziçi University.
 
The views expressed by the author are not necessarily those of Ernst & Young LLP or other members of the global EY organization.