How AI and automation are redefining the future of life sciences

An AI-powered platform accelerates drug discovery by screening 60 billion compounds and offering suggestions for synthesising new drugs. A new structural class of antibiotics was discovered after decades by a Deep Learning model running on an AI platform. AI optimised manufacturing is expected to reduce pharmaceutical production costs by up to 10 percent.

From clinical trials to laboratory workflows to commercialisation and compliance, Artificial Intelligence technologies are transforming end-to-end operations in the pharmaceuticals sector. Packing huge value potential, the $3.05 billion AI in pharmaceuticals market is projected to grow at a robust 42.68 percent CAGR to cross $18 billion by 2029.

Pharmaceutical and life sciences organisations have a seemingly unending list of AI use-cases to choose from to unlock wide ranging benefits, such as productivity, speed to market, innovation, personalisation and regulatory compliance. Let’s take a look at some of them:

Analytical and predictive capabilities accelerate drug discovery

Drug discovery is a complex, long drawn out, prohibitively expensive exercise, with very low conversion rates: drug discovery and clinical trials typically take more than a decade to complete; barely a handful of several thousand new drug candidates tested at the beginning qualify for clinical trial; a small fraction eventually receive approval for use on patients. But now AI is revolutionising drug discovery by analysing massive chemical and biological datasets to identify molecular patterns and potentially suitable drug candidates. It helps to design safer and more efficacious drugs by predicting the properties of drug molecules. In clinical trials, AI applications are not only helping to recruit suitable patients and optimise trial design by predicting outcomes and facilitating drug repositioning, but also improving trial speed, precision and cost efficiency. Helping drug companies compose cohorts, monitor activities, retain patients, and close trials, AI is now an inseparable part of the clinical trial process.

Automation optimises crucial supply chain operations

AI’s predictive and automation capabilities play a crucial role in the pharmaceutical and life sciences supply chain. Integrating the supply chain to provide complete visibility into operations, AI helps companies forecast demand more accurately, make better inventory decisions based on warehousing and demand, drive efficiencies across production activities, and improve compliance. Smart automation leveraging sensors and cameras is enhancing quality assurance operations. Automated supply chain management saves costs, reduces waste, builds resilience and enables real-time monitoring of distribution, which is particularly important in the case of sensitive medications.

Insights drive personalised treatments

By analysing medical datasets – patient records, lab test results, medical imaging – AI can identify patients at risk of developing different diseases, to aid early detection and timely intervention. This insight may also be used for personalising treatment plans for individual patients based on their health parameters, response to past treatments, etc. Medical natural language processing enhances AI’s predictive capabilities, uncovering hidden patterns in medical documents and reports quickly and accurately to bring new insights to light.

Content creation capabilities support researchers to compliance managers

In 2023, a leading consultancy estimated that Generative AI could create annual economic value in the pharmaceuticals and medical products industries to the tune of $60 billion to $110 billion. The technology is extending the impact of AI on the pharmaceutical and life sciences business by introducing new use cases or expanding existing ones in the areas of knowledge extraction, content creation, customer engagement and software generation. For example, Gen AI can reduce the time and effort scientists spend on extracting and summarising information from scientific documents, trial data etc. by automating these tasks; what’s more, users can even query it using natural language. Co-pilots powered by Gen AI assist clinical study teams by offering relevant insights through conversation, automatically drafting communications between cross-functional teams, and issuing intelligent alerts for proactive intervention. The technology also enables regulatory compliance tasks, such as responding to questions from health authorities during clinical development by predicting the type of queries that may arise, drafting suitable replies, and offering a variety of insights.

Offsetting these possibilities are the usual challenges of AI – lack of transparency and explainability, poor data quality and availability, and various ethical and regulatory risks. Being highly accountable to both regulators and consumers, and facing huge liability risk, pharmaceuticals and life sciences companies need to be particularly vigilant when deploying AI. Robust governance and a Responsible AI framework are essential for ensuring adherence to regulatory mandates, data protection rules, and ethical principles, such as avoidance of bias in algorithmic outputs. By playing it right, organisations can revolutionise the business to improve outcomes for all.

Views are personal. Author is EVP and Global Head of Life Sciences at Infosys