How is AI used in life science?
It’s clear that the growing role of AI in revolutionising health, biotech and medicine is here to stay. If you’re still asking, “How is AI used in life science?” you’re probably not paying enough attention to the trends. As this Science Direct paper explains, it’s hard to deny the “great power of AI in promoting life science research. It is evident that the field of life science research is one of the pioneers in generating big data and embracing AI methods, especially through the development and application of bioinformatics. On the other hand, the employment of AI methods, in combination with the big-data generation technologies, has transformed life science research into a new paradigm.” But it’s not without its challenges.
The impact of AI in life sciences
AI is great at processing large amounts of data, looking for patterns and doing it all much faster than a human expert could. You’ll find AI tools like ours in spaces like drug discovery, disease diagnosis, genomics analysis, personalised medicine, medical imaging, protein structure prediction, biomarker identification, clinical trial optimisation, epidemiological modelling, patient data analysis, lab automation, synthetic biology design, health monitoring, vaccine development and agricultural biotech.
When done ethically, AI is:
- Faster and more accurate than humans
- Able to scale well to deal with large data volumes
- Doing the mundane tasks, so humans don’t have to
- Enabling people to do more creative and enriching work, as a result
But that doesn’t mean AI adoption in the life sciences is without risk.
Risks of AI in life science
The models need to be trained properly and tested before their results are used. There are some questions that AI is very good at answering, and others where traditional machine learning is better. It should also always be used transparently and honestly, with human oversight. Human and animal bodies are very complex. We explain, “Our knowledge and perception of the human body is constantly evolving and growing. Machine learning can optimise this to deepen our understanding and facilitate in progressing our modern-day medicine. Prediction models can then advance us further, identifying early risk indicators that could reduce [the] risk of disease and improve treatment protocols, ultimately strengthening our ability to fight and reduce human disease.” And there’s a possibility of missed causality or underlying factors due to this complexity. So, we never recommend using AI carte-blanche for critical interventions like diagnosis without human supervision.
The future for AI in life sciences
But as people begin to respect the power and limitations of AI, usage will grow even more. The big focus of the future is on predictive and preventive healthcare, powered by AI. To do this, monitoring needs to improve, and we’re expecting to see an increase in AI integrations with wearable and real-time health data devices. This will lead to growing partnerships between tech firms and biotech, pharma or research bodies.
And we’re ready to do what we can to help advance scientific knowledge globally through our powerful, ethical AI tools. Learn more about our work here.