Undoubtedly, digital transformation is now a widely accepted trend in the global life sciences industry. In the middle of new healthcare-linked challenges, life sciences companies know that they must keep pace with the latest digital technologies. That’s the context in which Generative AI (or GenAI) is being seen as a powerful digital tool that can revolutionize the industry.

In fact, research suggests that the question of whether to adopt GenAI or not has been convincingly put to bed. According to Deloitte Consulting, the question facing industry leaders today is whether GenAI will bring incremental improvements – or be the catalyst for a “radical” business transformation. Analysts like McKinsey estimate that GenAI is set to transform every aspect of the life sciences business model. This technology could generate between $60-$110 billion annually in economic value for pharma and MedTech companies.

This is probably why the U.S. Food and Drug Administration (FDA) has also identified the immense potential of AI technology in areas like:

  • Medical devices
  • Patient diagnostics and therapy
  • Commercial manufacturing operations

Let’s discuss 5 ways in which GenAI solutions can impact the life sciences industry:

 

  • Drug discovery and development

As compared to traditional methods, GenAI-powered drug discovery is both fast and efficient at every development stage including:

  • Identifying disease targets.
  • Designing novel drugs to work on those targets.
  • Determining the success percentage of clinical trials for new drugs.

According to this 2023 Forbes article, life sciences and biotech companies are using Generative AI to develop drug molecules. GenAI-powered models can be trained to find novel therapeutical targets from massive stores of biological and chemical data.

Among the recent developments, Insilico Medicine is developing AI-designed drugs for cancer and tumor treatment. Similarly, GenAI models can develop novel protein sequences – useful in protein engineering and novel therapeutics. For example, Google DeepMind’s AlphaProteo is now capable of designing novel proteins that can bind to target molecules.

 

  • Clinical trials

Previously, 86% of clinical trials failed due to insufficient patient samples. Additionally, life sciences companies struggled to manually recruit patients on time for clinical trials. 

With the emergence of GenAI, drug researchers find it easier to locate eligible candidates based on:

  • Demographic data
  • Previous medical treatments
  • DNA sequencing data

Effectively, Generative AI models can create synthetic patient data for simulating efficient clinical trials – without any manual intervention. Additionally, life science companies are using GenAI to work on treatments of rare diseases like:

Using historic medical data, GenAI can also address the problem of rising patient dropouts at on-site clinical trial facilities. This is possible by clustering patients based on their risk levels and phenotypes.

 

  • Assistive devices

Beyond drug development and clinical trials, Generative AI is also applicable in the realm of assistive medical devices. Among the applications, AI-powered wearables can track health metrics for individuals including their vitals and adherence to prescribed medication. Additionally, AI can also personalize recommendations to promote individual health and well-being.

Further, medical researchers are working on AI-powered innovations from prosthetics to brain implants. Among the recent developments, a paralyzed patient underwent surgeries for:

  • A brain implant that used AI to read the mental thoughts of the patient.
  • An abdominal implant causes muscular contractions that trigger the patient’s body movement.

Going forward, Generative AI will advance assistive technology, which will benefit over 2.5 billion people – projected to rise to 3.5 billion by 2050.

 

  • Research work

The National Human Genome Research Institute notes that “research scientists now have access to massive datasets, including genome data. Genome research is expected to generate between 2-40 exabytes of data over the next decade. Hence, researchers need GenAI and machine learning tools to interpret the information.

With Generative AI, research teams also find it easier to collaborate and share AI models, data, and research findings. By integrating GenAI into research work, life sciences companies can instantly perform data analysis, which previously used to take weeks or even months. This helps them accelerate the pace of:

  • Disease detection and cure
  • Recruitment for medical trials
  • Drug discovery

Additionally, GenAI is automating pharmacovigilance by collecting and analysing data from patients reporting side effects from new drugs.

 

  • Biotech applications

The market value for Generative AI in the biotech industry is set to grow from $54 million (in 2022) to $472 million (in 2032). This growth rate highlights the popularity of this technology in this specialized domain.

In this LinkedIn post, Zareh Zurabyan of eLabNext suggests that GenAI can help in:

  • Promoting a divergent thinking process.
  • Challenging human bias.
  • Facilitating collaboration among research teams.

Besides drug discovery, GenAI has multiple applications in the biotech domain such as protein engineering and personalized medicine. For instance, by analysing the genetic information of the patient, it can customize their treatment procedures and minimize adverse reactions from new drugs.

Going forward, Generative AI in biotech laboratory operations can automate the generation of scientific literature.

 

Conclusion

As compared to other digital technologies, Generative AI has generated more business value in a shorter period. In this article, we have only covered some of the ways in which GenAI is transforming the life sciences industry. 

With its expertise as a life sciences solution provider, Trinus is enabling pharmaceutical and biotech companies to address their unique business challenges and explore opportunities. Some of our key projects in this sector include:

  • Clinical process improvement
  • Digital health and product development
  • Clinical trial management
  • Laboratory information management

If you are looking for an experienced solution provider in your industry domain, contact us now.