From Data to Insights: AI’s Transformative Influence in Life Science


AI is revolutionizing every industry, including playing an increasingly important role in life science. Many companies are leveraging the power of AI to make advances across research, drug discovery, and clinical trials, which is in turn reducing the time and cost of bringing a new drug to market. 

This article looks at the transformative influence of AI we’re already seeing in five key areas of life science.

1. AI and genomics research

Genomics research involves analyzing DNA sequences to understand how genes function and how they interact with each other. Extensive complex datasets are generated that AI can analyze and interpret more accurately and efficiently than traditional methods, recognizing patterns, detecting genetic variations, and predicting disease risks. 

Researchers are then able to pinpoint disease-causing mutations and potential drug targets much faster, and make informed decisions across a variety of genomics tasks like variant calling, genome annotation, and functional impact prediction. 

2. AI and drug discovery

AI accelerates the drug development process by streamlining the identification of new drug candidates, and predicting their efficacy and safety. AI algorithms analyze databases of existing compounds or natural products, and they also have the potential to design and synthesize new compounds. 

AI also recognizes connections between diseases, genetic factors, and potential treatments, which can inspire researchers to consider innovative new therapeutic approaches. AI-driven collaboration platforms allow the sharing of data between researchers worldwide, with clear IP management, encouraging a coordinated global effort to tackle pressing life science challenges. 

3. AI and clinical trials

Clinical trials can be time-consuming and expensive, slowing down the drug development process. Utilizing AI has been shown to vastly improve the speed, efficiency, and accuracy of this crucial step, helping researchers to make informed decisions about the design of trials, and the selection and monitoring of patients.

AI automates many of the tasks that would typically be performed by researchers, such as analyzing large datasets, and spotting correlations and patterns that would be difficult for humans to detect. 

AI can also help improve patient recruitment and retention through predictive modeling that helps identify the patients most likely to benefit from a particular treatment, and target them specifically. This reduces the number of patients who drop out of the trial, and consequently improves the quality of the resulting data.

Additionally, AI-powered wearable technology allows researchers to track patient progress in real-time, and identify potential side effects more quickly. It also means patients don’t need to visit a physical site as monitoring can be done remotely.

4. AI and drug repurposing

Through analysis of the chemical structures and properties of existing drugs, and then comparison with information about diseases and biological pathways, AI can identify existing drugs that may be effective in treating other medical conditions. 

Drug repurposing is done to potentially save time and money in the drug development process, and to bring effective treatments to patients more quickly, as any existing compounds that have already demonstrated safety in humans don’t require Phase 1 clinical trials.

5. AI and natural language processing (NLP)

NLP is a powerful AI tool that quickly analyzes and extracts relevant information from medical texts, such as research papers and clinical trial data. In addition to saving the time and resources it would take to do this manually, NLP can help researchers identify patterns and insights they may not have been aware of before.

It was an AI platform using NLP that recognised the first cases of COVID-19. On the 30th of December 2019, BlueDot, a Canadian-based AI platform, identified a cluster of pneumonia-like cases in Wuhan and noted similarities with the SARS virus. Coincidentally, the founder of BlueDot had been inspired to start the company after his experience treating patients in Toronto during the SARS outbreak in 2003. 

BlueDot’s company mantra is to ‘spread knowledge faster than the diseases spread themselves’, and the platform sends out alerts of any anomalous disease outbreaks it detects, and the risks posed, to healthcare, government, business, and public health clients.

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