Driving Innovation and Efficiency with Gen AI in Life Sciences

AI has profoundly impacted the life sciences industry for the past couple of decades. In the 2000s, researchers were able to use AI to analyze the human genome, identifying genetic markers and variations that could predict an individual’s susceptibility to certain diseases. This opened the door to personalized medicine and more effective therapies for genetic disorders. In the 2010s, organizations extensively used machine learning models that could predict how different compounds would interact with biological targets, significantly reducing the time and cost of drug discovery and development. 

Today, generative AI-powered tools and algorithms are being used for diagnostics, predicting disease outbreaks and targeted treatment plans — and the industry is just getting started. According to estimates, gen AI will create between $60 billion and $110 billion annually in economic value for pharmaceutical and medtech companies. AI in drug research alone is expected to expand by 36% by 2031. Meanwhile, 79% of industry professionals said gen AI has the potential to revolutionize drug manufacturing in terms of quality and efficiency. 

Making way for gen AI innovation

But before realizing the value of gen AI, life sciences organizations must clear significant roadblocks. These include security concerns and data privacy regulations, data access and management issues, and the complexities of implementing AI solutions into existing life sciences workflows. Additionally, there are high costs associated with implementing AI and maintaining a team of skilled professionals to develop and manage AI systems.

Organizations that overcome these challenges and successfully implement gen AI in their operations can achieve a wide range of benefits. They can accelerate research and discovery, optimize manufacturing, enhance commercialization, reduce costs and, ultimately, improve patient outcomes.

First, it’s important to understand how life sciences companies can harness gen AI to drive innovation and efficiencies, and to determine the pros and cons of building versus buying a gen AI solution. 

How gen AI can power success in life sciences

The life sciences industry is uniquely positioned to receive the transformative benefits of gen AI. Life sciences generate enormous amounts of data that gen AI can harvest to uncover new insights. The industry deals with highly complex biological systems and processes, which gen AI can model and simulate to achieve clinical breakthroughs. And costly, time-consuming processes that involve rote tasks, such as drug discovery and manufacturing, are ripe for automation and increased efficiency. 

Here are three key ways life sciences companies can use gen AI to accelerate research and development, optimize manufacturing and enhance commercialization.

1. Accelerate research and development

Gen AI can make the drug discovery and development process faster, more efficient and more cost-effective, ultimately driving innovation. Gen AI uses advanced algorithms to analyze vast data sets of chemical compounds and biological targets, identify novel proteins or genes for therapeutic intervention, and leverage modeling and content creation to suggest new drug candidates with tailored properties. Once drug candidates are identified, gen AI can expedite their time to market by streamlining and automating processes. This efficiency allows clinical research professionals to focus on strategic planning and cohort management, ultimately driving more innovative therapies and products.

Here are some examples of how gen AI can speed research and development:

• Drug discovery models: Models for protein structure prediction, chemical property prediction and molecular docking streamline the design and testing of new drug candidates, including protein folding.

• Medical imaging: Embedding models in AI can identify disease markers in images, helping with early diagnosis and treatment.

• Synthetic data generation for secondary analysis: AI can create artificial data that mimics real-world data, which is useful for secondary analysis, where researchers can validate their findings without the constraints of patient privacy. 

• Clinical text summarization: Using natural language processing, gen AI can ingest and process large volumes of clinical data and efficiently extract important information. 

• Audit preparedness: Gen AI can assist in audit preparedness, particularly with FDA 483 observations, by drafting correspondence and automating data analysis and document review. 

2. Optimize manufacturing and supply chain

With gen AI, life sciences manufacturers can save costs, enhance product quality and accelerate time to market. Gen AI can analyze large data sets of pharmaceutical manufacturing processes and quality control data to improve production efficiency, minimize waste and optimize supply chain management. 

Examples of how gen AI can improve manufacturing and supply chain processes include:

• Cycle-time analytics: Gen AI’s large language models can analyze vast amounts of production data to identify patterns and pinpoint inefficiencies.

• Demand forecasting: AI can forecast demand for medicines, helping to avoid overstocking or shortages.

• Routine task automation: AI solutions can automate routine or repetitive tasks, reducing the need for manual intervention.

• Predictive analytics: AI models can use data, such as weather forecasts and market indicators, to predict potential quality issues, bottlenecks and disruptions in the supply chain, allowing companies to take proactive steps.

• Resource utilization: Gen AI can use real-time data from production machines, inventory systems and supply chain partners to ensure raw materials are used efficiently and waste is minimized.

• Energy optimization: AI-driven solutions can ingest power meter and machine data and suggest areas for improvement and cost savings.

3. Enhance commercialization

Gen AI can help life sciences companies improve efficiencies, personalization and regulatory compliance as they bring a new product or therapy to market. Gen AI analyzes massive amounts of data from various sources, such as market research, sales data, regulatory documents and healthcare databases, to optimize sales and distribution processes and ensure successful product launches.

Below are several ways life sciences companies can use gen AI to boost commercialization efforts:

• Targeted campaigns: AI can analyze social media, surveys and market reports to segment audiences based on their behavior and preferences and create more personalized campaigns. 

• Content generation and tagging: Gen AI can create customized text, images and video, and perform content tagging of large data volumes to enhance personalized marketing and improve documentation for regulatory compliance.

• Virtual assistants: AI-powered chatbots can support sales teams by answering questions and providing real-time information during sales calls.

• Sales summaries: AI can generate concise and personalized precall summaries for sales reps as they engage with healthcare professionals.

Should you build or buy a gen AI solution?

Once company leaders identify the use cases for their gen AI efforts, they can choose to either develop a custom gen AI solution in-house or purchase a prebuilt solution. This decision is based on multiple factors. They may have a limited budget or a tight timeframe for bringing a drug to market. They may have trouble finding skilled professionals to build a system. Or they may need to meet more complex project requirements, such as integrating specific genomic or clinical trial data sets, or customizing gen AI models to address unique therapeutic areas or regulatory compliance needs.

Here are the main pros and cons of each strategy. 

Buying a solution

Ready-made gen AI solutions reduce time to value, allowing companies to quickly leverage AI for tasks such as de novo drug design, patient stratification and biomarker discovery. Buying an existing solution often requires a lower initial investment than building an AI solution since the development and training phases, including model training and hyperparameter tuning, have already been completed. Commercial solutions are developed by specialized vendors with deep expertise in AI, and most come with features to reduce or eliminate hallucinations and data leakage into the models — crucial in life sciences, where predictive accuracy and model robustness are paramount. Finally, vendors provide ongoing maintenance and support, ensuring the solution remains up to date with the latest AI advancements, such as transfer learning and reinforcement learning.

On the other hand, off-the-shelf solutions have limited customizations and may not meet a life sciences company's specific needs and workflows. Customizing neural network architectures or fine-tuning models to meet specialized research needs can be challenging. There may be concerns about data privacy and security, especially when dealing with sensitive patient information. Ensuring compliance with regulations, such as HIPAA, and implementing differential privacy measures can be challenging. Vendor dependence for updates, support and troubleshooting can become a risk if the vendor’s priorities change, affecting model retraining and algorithmic updates. And there may be integration challenges with existing systems and data. For seamless integrations, Snowflake Native Applications are recommended because they are tailored to specific platforms and don’t require data to be moved to a third party, which can improve the user experience and support data protection and compliance requirements.

Building a solution

Building in-house allows for the development of customized gen AI models that are tailored to specific data sets and use cases, such as drug discovery or personalized medicine. Managing sensitive data internally can strengthen data privacy and security, reducing the risk of data breaches that might occur with third-party solutions. In-house development gives companies control over how data is stored, used and protected. Custom AI solutions can be designed for native integration with existing systems, such as EHRs and LIMs, minimizing disruptions and maximizing efficiency. Finally, organizations have flexibility and control over the model training process, including the choice of algorithms, hyperparameters and training data. 

Conversely, developing and maintaining a custom gen AI solution can be complex and expensive in terms of time, money and resources. This includes costs for data collection, model training, infrastructure setup and algorithmic updates. Creating and maintaining a gen AI solution requires new skill sets and specialized resources. The scalability of a custom-built solution to accommodate growing data and computational biology tasks can be limited. 

The power of data and AI

Life sciences companies must consider many factors when determining the reasons and strategies for implementing Gen AI. Snowflake can help life sciences companies develop a strong data foundation and data strategy — the building blocks of gen AI — whether they decide to build or buy a solution. Snowflake’s mission is to provide the platform and tools to enable organizations to access and mobilize the healthcare and life sciences ecosystem of data and solutions. By doing so, companies can accelerate meaningful innovation to improve outcomes for patients everywhere.

For more information on how Snowflake can help your life sciences organization harness the power of gen AI, read the ebook, Gen AI in Healthcare and Life Sciences: 4 Things You Need to Know, and watch the life sciences industry roundtable, How Data & AI Pave the Way for Innovation.