The role of AI in life sciences businesses: Transforming operations and R&D


The life sciences industry has only really scratched the surface of AI’s potential. To date, the primary application of AI has been to transform operations through automation of existing processes, but there is so much more scope. 

This article looks at how AI already enhances efficiency in life sciences, and what companies can do to leverage the technology in order to reach the predicted global market of $36.1 billion for AI in life sciences in 2025, up from $902.1 million in 2017.

Where AI is improving process efficiency

AI is helping transform processes in life sciences, adding value across R&D, clinical trials, disease detection and diagnosis, manufacturing and supply chain, and safety. 


AI has been successfully applied throughout the drug discovery process, including in the following steps:

  • Target discovery and identification
  • Virtual screening
  • Lead optimization of drug candidates 
  • Drug repurposing 
  • New drug combination analysis

Clinical trials

AI can be leveraged to support the design, recruitment, and analysis of clinical trials.

Identification of treatment response predictors can be developed using historical trial data, which helps dictate the trial design. AI can then assist with identifying patients for enrolment based on their eligibility, plus identification of target populations, and estimation of the ideal trial size. AI may also be used to support the automation of data entry and other admin tasks once the trials are underway.

Disease detection and diagnosis

AI is used in image analysis of X-rays, MRI, and CT scans, particularly in the detection of neurodegenerative disorders, heart disease, and cancers. A good example is spotting signs of breast cancer in mammograms. By training the AI model on thousands of de-identified mammograms, i.e. with personal information removed, it learns the complex features that are likely to represent cancer, enabling it to detect signs that specialists might miss.

AI can also be used for predictive modeling, utilizing clinical and demographic data to detect important pieces of patient history efficiently and identify individuals who may be at risk of certain illnesses. Additional future applications of AI in this area are likely to include less invasive monitoring approaches through virtual biopsies or monitoring of vital signs.

Manufacturing and supply chain

AI can be leveraged in many ways within manufacturing, including monitoring QC to ensure the quality of products created to match certain criteria, such as shape and size. It can also be used to forecast demands, allowing for production to be scaled accordingly, and its application in smart packaging can even help limit counterfeit drugs from reaching the market.

Improving supply chain visibility and adaptability is crucial, as many life science companies still respond reactively to disruptions, and need to be faster to adjust to changing inventory and production levels. AI has the potential to automate data analysis to predict demand and supply, enabling companies to immediately react to changes in market demand and supply so they can quickly recover from disruptions.

Minimizing safety risks

AI can protect patients by recognizing medication errors that would otherwise go undetected. An example is MedAware, a new AI-powered safety monitoring platform that detects and prevents medication-related risks and errors, flagging conflicting medications and potential adverse drug events with 92% accuracy. 

The increasing impact of AI in life sciences

In September 2023, the Pistoia Alliance, a not-for-profit group promoting greater collaboration in life sciences R&D, presented the results of a new global study at the Lab of the Future conference in Amsterdam. 

The study, conducted in partnership with Open Pharma Research, involved 200 experts from Europe, the Americas, and APAC being surveyed about lab technology investment, barriers, and benefits. 

60% of respondents said AI will be the top investment in life sciences over the next two years, with more than half (54%) of labs already using AI. 58% said that low-quality datasets are the number one barrier to implementing AI, and privacy and security concerns around data were raised as a challenge by 34%. 

55% of respondents said that best practices and business use cases would help them integrate AI in their lab. 39% would like more educational courses and 36% said they would like the opportunity to collaborate with other organizations to share AI knowledge and risks.

The results show that wearables, virtual reality, and augmented reality technologies will be a key feature going forward, with 40% of respondents expecting to be using this equipment in the next two years. Other future investment priorities include cloud computing platforms (51%), and robotics and automation (36%).

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