The State of Generative AI in Life Sciences: The Good, The Bad and The Ugly


An Overview of Key Findings 

Axendia recently released its report, “The State of Generative AI in Life Sciences: The Good, the Bad and the Ugly.”

This report highlights the positive impact of generative AI in life sciences, with key focuses on the benefits, adoption rates, opportunities, and challenges that come with it.

Below are a few key findings:

Respondents’ Current Knowledge and Comfort Level 

Axendia’s research found that 36 percent of industry professionals are very familiar with generative AI, whereas 27 percent are still in the learning phase. This shows there is an opportunity for strategic partnerships and educational initiatives to close this gap.

Additionally, when it comes to C-level executives, 82 percent consider themselves to be very familiar with generative AI. This is perhaps due to their desire to remain ahead of the pack in emerging technologies.

Although comfort levels with generative AI may vary, those who are willing to learn more about it will be in a better position to implement it effectively within their organizations.

According to industry advisory board member, Rex VanHorn, Sr AD, IT Technical Architect at Boehringer-Ingelheim, “AI technology is not new, but the ability to implement it at scale in the life science industry is. It’s not necessarily that personnel in varying roles across organizations aren’t currently familiar with GEN AI but rather they need the chance to learn it.”

The Role of Generative AI in Drug Discovery

Axendia’s research shows that 78 percent of respondents think generative AI will revolutionize drug discovery.

This report shows that target identification is the most anticipated impact of generative AI on drug discovery.

AI algorithms can analyze incredible amounts of data from various sources, including genetics, proteomics, metabolomics, and clinical trials, to identify novel drug targets and potential drug candidates. AI also helps by designing more effective drugs with fewer side effects. This is possible by using computational methods to predict the properties of a drug molecule, such as its solubility, toxicity, and bioavailability.

Additionally, more than half of respondents surveyed by Axendia indicate that the most promising use case for generative AI in clinical trials is predictive modeling for drug safety and efficacy.

The top three barriers to incorporating generative AI in drug discovery include bias and ethical concerns, regulatory compliance concerns, and data accuracy. These challenges show the need for considering ethical frameworks, effective compliance strategies, and incorporating data management into the integration of generative AI.

Implementing Generative AI in Medical Device Manufacturing

Axendia also surveyed respondents with responsibility for medical device manufacturing. While 71 percent believe that generative AI may revolutionize the field, only 13 percent currently use it.

Key areas include process improvement and efficiencies, of which 64 percent are considered to be impacted the most by generative AI. Following behind at 55 percent and 50 percent respectively, are planning and scheduling and quality assurance and testing.

Regarding the top barriers, the research shows that medical device manufacturers are most concerned with data integrity and governance, regulatory compliance, and data security. Bias and ethical issues were also prevalent in the results. This indicates that to successfully implement generative AI in medical device manufacturing, manufacturers must navigate through ethical, operational, and regulatory challenges.


Reaping the Benefits of an AI-Powered eQMS in Life Sciences

Axendia’s research reveals the potential of generative AI to improve industry processes and product lifecycles. Solutions like AI-powered electronic quality management systems (eQMSs) provide substantial opportunities for enhancing efficiencies, like improved analytics and predictive maintenance. The result is greater innovations in drug development, clinical trials, and personalized medicine.

Dot Compliance was pleased to support this research report. By downloading the full report, get deeper insights and practical and actionable advice on adopting generative AI.

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