Artificial intelligence (AI) is changing the world way faster than most of us could imagine. Computers have never been so powerful. Computing costs have never been so low.
Hence now is the pivotal moment for using AI in life sciences.
Life sciences and healthcare are on the list of top industries that are actively embracing next-gen AI technologies such as generative models, natural language processing, computer vision, and reinforcement learning. According to a survey conducted by Deloitte in 2023:

  • 50% of healthcare and life sciences organizations are currently applying next-gen AI
  • 36% of healthcare and life sciences organizations are planning to use next-gen AI in the next year

In this article, we’ll sum up the main challenges the industry is facing. Also, we’ll discover how AI can solve them by highlighting the benefits of the technology and giving examples of the most influential artificial intelligence companies in life sciences so far.

The Main Advantages of AI for Life Sciences Companies

AI and life sciences are going together hand in hand. And that’s truly fascinating!
Google took the AI tools that are great for analyzing YouTube videos, applied them to problems that matter to health research, and advanced treatment for diseases that we still consider incurable using augmented reality microscope.
Also, the tech giant developed computer vision technology powered by machine learning (ML) to create a 3D wiring map of the human brain and get a better understanding of neurodegenerative disorders like Parkinson’s and schizophrenia.
Microsoft uses its Azure AI Infrastructure to help health and life sciences companies to get insights into clinical trials, medical imaging, genomics, and precision medicine.
IBM advances life sciences technology by helping research companies eliminate clinical development inefficiencies based on better AI protocols and streamlined operations.
AstraZeneca employs artificial intelligence to fight diseases more efficiently, identify new targets for new medicines, make predictions in the creation process of new molecules, improve projections of clinical success, and establish new approaches in the clinic.

How the Use of AI in Life Sciences is Changing Everything

The possibilities of AI are changing our perception of the human body and the concept of healthcare in general. The things we’ve seen in science fiction are now real AI use cases in life sciences.

1. Clinical Trials

Traditional clinical trials have many flaws waiting to be improved.

  • Challenge 1 — No Patient-Friendly Approach. Established clinical trial designs are far from being patient centric. Patient selection can often be substandard. Patient monitoring, retention, and adherence are substantial challenges because of the travel burden.
  • Challenge 2 — Outdated Data Management. The success of using clinical trial data still depends on manual processing, consolidating from multiple systems, and reworking the same complex datasets created from scratch. Clinical data management lacks analytical power.
  • Challenge 3 — Trial Cycle Speed and Costs. The challenges above lengthen clinical trials with no possibility of flexibility and spending optimization.

AI Solutions for Clinical Trials

Artificial intelligence provides life sciences companies with enormous possibilities for clinical trial transformation.
With the help of AI-enabled wearable devices, patients can share reliable results remotely, and in this way, simplify monitoring for trial managers. AI can consider patient behavior and preferences to create better experiences, increase retention, and determine standards for recruiting strong candidates.
ML can facilitate data management automation and help companies connect different systems into a single environment. Artificial intelligence can provide sufficient analytical infrastructure for reworking and reusing data without building databases afresh.
Thanks to AI algorithms, costly clinical development that usually took months can now take weeks with more than 50% off the whole cycle.

2. Drug Discovery and Development

Yahoo Finance reports that the drug discovery market will increase from over $627 million in 2021 to almost $4.2 billion in 2028, with a compound annual growth rate of nearly 42%.
Despite such an optimistic forecast, discovering effective drugs is still challenging.

  • Challenge 1 — It’s Time-Consuming and Expensive. Traditional drug discovery takes between 11 and 16 years and costs between $1 billion and $2 billion. In established drug discovery design, pharmaceutical companies have more chances to optimize resources only in the early stages of discovery.
  • Challenge 2 — It’s Very Difficult. Drug development requires the analysis of massive amounts of data. For example, biotechnology companies have to analyze thousands of healthy and sick human cell samples to identify disease mechanisms. And that’s only one of the multiple stages necessary for finding potential drug candidates.

AI Solutions for Drug Discovery

Next-gen AI platforms will provide scientists with machine learning models to help them generate entirely new approaches and ideas for new drugs and make their work in research labs 100 times faster.
By teaching algorithms and feeding them with relevant information, pharmaceutical companies will achieve greater accuracy of results, reduce drug spending, and take corrective actions to develop more effective drugs.

3. Precision Medicine

Precision medicine is an innovative method used to prevent and treat diseases on an individual level based on genetic, genomic, and multi-omics data science.
Contrary to traditional medicine for everyone, personalized medicine considers the peculiarities of every patient, such as family medical history, environment, and lifestyle.
Just as e-commerce sales reps target customers with the exact products they need based on their preferences and needs, companies in life sciences will use AI to offer patients the most effective drugs that match their genetics.

  • Challenge 1 — The Processing of Vast Amounts of Data. Researchers need to process biological and clinical databases — extensive amounts of high-quality data generated from a large number of locations and consolidated into a single source — to predict, treat, and reverse the individual risks of a particular disease accurately and efficiently.
  • Challenge 2 — The Determination of an Individual’s Health and Risks for Disease. To unlock the full potential of precision medicine and discover how diseases occur on an individual level, researchers need to understand how the environment and lifestyle of an individual affect their genotype. The best way to do that is through deep phenotyping that provides the individual’s molecule profile.

AI Solutions for Precision Medicine

More pharmaceutical companies are investing in big data and AI technology, such as deep learning, machine learning, and artificial neural networks, to increase accessibility of precision medicine to the life sciences industry.
For instance, one machine learning model analyzed data from 11,000 tumors from 33 cancer types to help scientists better understand why cancer mutates from origin cells and identify more effective ways of tumor modulation.
Another great example is an AI-powered mobile app that can provide users with personal nutrition recommendations by matching the microbiome analysis of an individual’s health and the app’s unique database.

4. Manufacturing, Supply Chain, and Value Chain

Life sciences supply chains are going through tremendous transformations.
In response to the pandemic, 83% of life sciences companies improved their approach to supply chain management, according to the 2023 Global Life Science Supply Chain Risk Report by WTW.
In a supply chain survey by KPMG, 85% of life sciences leaders claim that digital transformation will cause changing roles in the ecosystem.
Innovation brought by artificial intelligence has tremendous potential to multiply the effect of every opportunity from the list above by solving main supply chain challenges.

  • Challenge 1 — the Lack of End-to-End Visibility and Underused Data. Supply chains, where many stakeholders engage with each other in different ways, require a centralized approach to collecting, monitoring, and analyzing underused data.
  • Challenge 2 — The Lack of Supply Chain Integrity. The lack of end-to-end visibility results in a deficiency of transparency, poor organizational security, and insufficient traceability of materials and products.
  • Challenge 3 — The Lack of Predictive Maintenance. Life sciences supply chains need artificial intelligence systems to modernize human capabilities with real-time actionable insights about equipment performance, which will improve operations, detect manufacturing bottlenecks, and minimize production risks.
  • Challenge 4 — Stock Control and Logistics. AI in life sciences can help increase supply chain productivity and reliability by implementing intelligent warehousing, predicting customer demand and product supply, planning routes more effectively, and changing them based on various conditions.

AI Solutions for Supply Chains in the Life Sciences Industry

Powered by artificial intelligence, optical character recognition (OCR) technology can help life sciences companies automate document workflows and data entry, streamline operations, and boost productivity.
Google Cloud offers multiple AI/ML-enabled solutions to employ value chain principles and provide any life sciences company with end-to-end supply chain visibility, alert-driven event management, and next-generation collaboration across teams and partners.
For instance, a life sciences company can use pre-built machine learning models and tools to predict demand, detect product defects with domain-specific AI models, optimize planning and decision-making, and improve logistics with an AI-powered API.

The Future of Artificial Intelligence in the Life Sciences Industry

Artificial intelligence will keep digitalizing, modernizing, and optimizing life sciences. In the future, we’ll witness more demand for AI in life sciences and a broader application of AI-enabled technologies.
More companies will implement insights-driven analysis to facilitate clinical research and manufacturing and focus on developing value


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