How Artificial Intelligence and Machine Learning are Transforming Life Sciences

Today, the life sciences industry is at a critical inflection point. His public profile has risen due to his success in rapidly developing vaccines to fight the COVID-19 pandemic. He also formed a parcel of confidence. Despite the persistent problem of vaccine Hesitatingly, healthcare – including life sciences – climbed the rankings to become the second most trusted sector after technology, according to the Edelman Trust Barometer 2021.[1]

While the life sciences industry rightly has the approval and trust of its stakeholders – including healthcare companies, insurers, clinicians and patients – such approval gives rise to a significant challenge. for the future. This challenge responds to the ever-increasing expectations of these stakeholders.

The rapid development and mass deployment of COVID-19 vaccines, including pioneering mRNA vaccines, have demonstrated to stakeholders what the industry is capable of achieving. At the same time, new technological advances are opening up the possibility for the life sciences industry to make further breakthroughs that will transform patient healthcare experiences, while potentially saving millions of lives.

Transformation based on artificial intelligence and machine learning

With the maturation and advancement of artificial intelligence (AI), it is expected to have a measurable impact on the life science industry. AI is enabled by complex algorithms designed to make decisions and solve problems. Combined with machine learning (ML) and natural language processing, which enable algorithms to learn from experiments, AI and ML will help life science companies develop treatments faster and more efficiently in the future, thereby reducing healthcare costs, while making it more accessible to patients.

We already know that AI and ML have the potential to transform the following processes in the life sciences:

Drug development. With 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 them. bioactivity , toxicity and physico-chemical properties. Not only will this contribution speed up the drug development process, but it will help ensure that drugs deliver the optimal therapeutic response when administered to patients.

Diagnostic. AI and ML are effective in identifying features in images that cannot be perceived by the human brain. As a result, it can play a vital role in the diagnosis of cancer. Research from the National Cancer Institute in the United States suggests that AI can be used to improve cervical and prostate cancer screening and identify uncomfortable mutations from images of tumor pathology. There are already several commercial applications on the market. In the future, AI could 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 live longer, healthier lives.

Clinical tests . The way clinical trials were designed and conducted did not change significantly over the past decades, until the pandemic brought about the necessary changes to help transform some components of the clinical trial process, such as the study monitoring and patient recruitment. As the cost of research and development represents 17% of the total turnover of the pharmaceutical industry and has increased by 14% in the last 10 years,[2] there are calls for decentralization long overdue 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 corrective action timelines, reduce the group disposal cycle and investigate customer complaints. It can also monitor manufacturing processes online to ensure drug safety and quality. These interventions will give life sciences companies confidence that their manufacturing processes are performing to high standards and not putting the organization in breach of regulations. Importantly, the bottlenecks caused by the pandemic have tested the resilience of the entire supply chain ecosystem. Additionally, life science companies can improve efficiency by applying AI to their supply chain and logistics management processes, aligning production with demand, and with a sales planning process and AI-enabled operations.

Business and regulatory processes. Review of promotional content for compliance objectives has been a necessary but constraining gateway for any biopharmaceutical company. Current medical, legal, and regulatory review processes for approving product marketing materials are extremely slow and can be inconsistent, resulting in repetitive cycle times. Promotional content is the single most important source of information about newly approved products, given the paucity of peer-reviewed literature at launch. This prevents approved drugs from reaching providers and patients sooner. Now, AI and ML have been proven to be used to drastically reduce medical, legal and regulatory review time, while improving content accuracy. This will improve process speed and reliability, allowing therapies to get to market faster.

Beginning of a new digital era with wider use of AI and ML

We are only in the early stages of deploying AI and ML in the life sciences. And while we can already see their promise, the industry is likely to find many future use cases for the technology that we can’t even begin to design today. There are already early signs of how AI may 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 or not that happens will likely depend on regulatory frameworks and legal responsibilities, rather than technological advancements.

Inevitably, there will be a massive amount of change as we move past the current inflection point. Proliferating variants of the severe acute respiratory syndrome coronavirus, such as Omicron, and the successful deployment of mRNA technology leading to the rapid development of COVID-19 vaccines are pressuring the life sciences industry to do so. do more – and faster – when it comes to developing and manufacturing treatments for cancer and other diseases. So how can she meet this challenge? To meet the expectations of its stakeholders, the life sciences industry will undoubtedly have to exploit the full potential of AI and ML.

[1] Kristy Graham, “Science and Public Health: Transparency is the Path to Trust,” Daniel J. Edelman Holdings website, public-health# at top, accessed December 2021.

[2] Capital city IQ top 25 biopharmaceutical companies report, 2021.

Arda Ural, PhD, is the EY Americas Industry Markets Leader for EY’s Health and Wellness Sciences practice. Arda has nearly 30 years of experience in the pharmaceutical, biotechnology and medical fields, including general management, new product development, corporate strategy and mergers and acquisitions. Prior to joining EY, he was Managing Director at a strategy consulting firm and worked as Vice President of Strategic Marketing and BU lead in a medical technology 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.

Sherry J. Basler