Unleashing AI's Potential in the Life Sciences

2024/10/13: By Konstantin Sargsyan, CTO

Navigating the compliance and unstructured data maze

The life sciences industry, like many others, is grappling with the transformative power of artificial intelligence (AI). While AI has the potential to revolutionize drug discovery, clinical trials, and patient care, its adoption has been hindered by several significant challenges.

Data-Related Hurdles – and Unstructured data at that

One of the primary obstacles is the sheer volume and complexity of data generated in the life sciences sector. This data is often unstructured, siloed, and difficult to manage. Additionally, concerns about data privacy and security further complicate the adoption of AI.

Regulatory Barriers

The highly regulated nature of the life sciences industry presents another challenge. Compliance with regulations such as FDA 21 CFR Part 11 and Good Clinical Practice (GCP) guidelines can be time-consuming and resource-intensive. This can discourage companies from investing in AI solutions, fearing that the associated costs and complexities may outweigh the benefits.

Legacy Systems and Technology Debt

Many life sciences organizations rely on legacy systems that were designed decades ago. These systems may not be compatible with modern AI technologies, hindering their ability to leverage the power of AI. There is a lot of “Don’t move my cheese” in the industry which creates immense technical debt accumulated over time that can make it difficult to implement new technologies.

Fear of the Unknown

The fear of the unknown is a common barrier to AI adoption. Many organizations are hesitant to embrace new technologies, particularly when they involve complex concepts and potential risks. This fear can lead to resistance to change and hinder progress. Bad enough life science practitioners need to know their business and are learning technology as well but now we want them to be statisticians and AI specialists as well.

The Path Forward

Despite these challenges, the potential benefits of AI in the life sciences industry are undeniable. To overcome these hurdles, organizations must:

• Be kind and patient and bring people along: As a technologist I am acutely aware of when eyes glaze over when I geek out about some tech or model. I focus a lot more now on keeping things simple and educating my customers. Encourage experimentation and risk-taking, and provide employees with the training and resources they need to succeed.
• Invest in data management and governance: AI is not a magic wand that you can just sprinkle in this industry and realize immediate results. One needs to implement robust data management and governance practices and ensure compliance with data privacy and security regulations.
• Modernize IT infrastructure: Upgrade legacy systems and adopt cloud-based solutions to support AI applications. This is an ongoing area for legacy Biopharma – care needs to be taken to ensure the right partners are picked (sometimes consolidation here can lead to “vendor lock-in”)
• Plan for scale and production from the outset: Partner with AI experts and technology platform builders who know how to convert a POC into a sustainable product in a production setting. There are many customers who are jaded by flashy POCs that don’t turn into anything material. Ask the tough questions to ensure the solution actually solves the business need, results in ROI (Not just moving money around from one spend type to another), and can be built to scale.

By addressing these challenges and embracing AI, the life sciences industry can accelerate drug discovery, improve patient outcomes, and stay ahead of the competition. This makes me excited to come in to work every day. Happy to chat about this some more – Drop me a line.