Guest Column | March 19, 2024

Exploring Functional Uses Of AI In Life Sciences

By Latika Mahajan, The Smart Cube

Latika Mahajan

As generative artificial intelligence (GenAI) continues to permeate the life sciences industry, several key applications have risen in adoption — and have seen the most impact — within the industry. Below, we explore the key trends, outlook, and insights across five different applications, in five different functions, of GenAI within the life sciences industry.

Drug Discovery And Development

GenAI is transforming the biopharma industry by accelerating drug discovery and development. This is the most commonly used application of GenAI we have seen so far within the industry; use cases are spread across the entire biopharma value chain, from R&D to corporate functions.

We’ve seen platforms that streamline processes like protein engineering, as well as robotics and microfluidics. Oxford University has rolled out an AI tool that enables vaccine design by predicting viral mutations and evolution in response to the human immune system. The tool was also able to predict which antibody-based therapies would lose their efficacy with the progression of a pandemic or viruses developing mutations.

Clinical Trials

Another rising application of GenAI in life sciences is in clinical trials. GenAI is becoming increasingly valuable in several aspects of the clinical trial process, including patient selection, research design, post-trial analysis, and patient data handling.

GenAI can help biopharma companies by optimizing clinical trial protocols, which can improve patient recruitment accessibility and reduce overall trial durations, enabling quicker access to new therapies. AI can benefit organizations by forecasting adverse drug events, monitoring patient behavior, and patient onboarding and retention.

We’ve seen organizations like Unlearn apply GenAI to simulate virtual patient populations for clinical trials through the use of AI-powered digital twin generators. This showed reduced trial delays and failure rates, and effectively predicted health outcomes.

Manufacturing And Supply Chain

GenAI is driving efficiency and agility in the life sciences supply chain through several process improvements, including inventory and resource planning, delay reduction, and avoiding overstocking of inventory.

GenAI optimizes the pharma supply chain by forecasting demand for medicine and enhancing the efficiency of manufacturing processes. It can also be implemented to align resources to minimize waste and idle time, while ensuring precise medicine composition.

We’ve seen this with Sanofi’s AI platform, Plai, in collaboration with Aily Labs, which enabled Sanofi to accurately predict 80% of low inventory positions, and respond to them, in the drug development supply chain.


GenAI will continue to play a crucial role in commercialization processes for pharma companies. It can be used to determine patient and physician sentiments, optimal drug price points, and even potential brand names for drugs. This is critical because biopharma companies typically prefer suppliers who can support them on the entire marketing journey, including sentiment analysis, predictive analysis, and building web apps/HCP portals.

We’ve seen use cases like Sorcero’s AI platform, which offer omnichannel views of the healthcare provider journey, that are also able to simplify complex scientific data with plain language summaries to boost scientific engagements.

Monitoring And Surveillance

Another area of widespread GenAI use within the biopharma industry is medical chatbots, which are used to gather patient symptoms and provide meaningful recommendations. GenAI’s enhancements to patient monitoring chatbots have improved patient engagement and adherence to treatment protocols, while enabling streamlined delivery of information.

GenAI has been used in healthcare to provide personalized medical advice, improving patient outcomes and reducing treatment costs. Mayo Clinic’s deep learning algorithm, a GenAI application in healthcare, accurately predicts post-surgery complications and has the ability to tailor treatment plans.

2024 Outlook

The future of AI in the life sciences industry has transformative potential in many different sectors. AI will significantly speed up the creation of personalized treatments and drug discovery, shortening the journey from research to clinical use. Advancements in AI will lead to more accurate medical diagnostics and extend healthcare services to rural areas. As AI continues to develop and is embraced by more organizations, emphasis will be on addressing data privacy, ethical considerations in AI applications and adapting to changing healthcare regulations. By embracing AI-driven technologies, pharma companies, research institutions, and healthcare providers can unlock new opportunities for growth, improve patient outcomes, and advance the frontiers of medical science.

About The Author:

Latika Mahajan is an Assistant Manager in the Life Sciences sector at The Smart Cube. Latika is responsible for end-to-end execution of projects involving life sciences strategy. She is an MBA graduate from NMIMS, Mumbai, specializing in pharmaceutical management.