Accelerate breakthroughs at every stage of the pharmaceutical life cycle with trustworthy AI

Speed the identification of new molecules for drug development with AI, streamlining the science-heavy process that requires millions of data points.

The value of this solution:

• Faster decision making.
• Trustworthy insights.
• Competitive advantages.

AI techniques used in this solution:

• Machine learning is used to analyze large amounts of data to improve the efficiency and success rate in drug discovery.
• Large language models (LLMs) are used to identify drug targets and predict drug interactions.
• Synthetic data is used to fill data gaps, simulate trials and protect patient privacy.

How AI helps:

• Analyze large data sets using AI to more rapidly identify drug targets.
• Generate and capitalize on synthetic data to understand molecular interaction.
• Predict the safety and efficacy of drug candidates to help prioritize compounds and streamline pre-clinical testing.

The AI models provide:

• Synthetic data offers a unique opportunity to gather more insights, quicker.
• Algorithms are based on scientific relevancy.
• Models help to improve the drug development process materially.

Improve the efficiency of clinical research while protecting the safety of study participants with predictive analytics using AI and digital twins.

The value of this solution:

• Improved safety.
• Accelerated innovation.
• Faster decision making.

AI techniques used in this solution:

• Predictive analysis is used to identify which patient populations will respond best to future drugs.
• Digital twins are used to simulate drug interactions, identify candidates for drug repurposing and understand alternate pathways for patients.

How AI helps:

• Improved understanding of diseases, patient populations and drug interactions and efficacy.
• Accelerated clinical research while ensuring patient safety.

Use models to streamline the clinical trial protocol process by transferring information and making material “protocol-ready” to fit into a template, saving hours of manual drafting for clinical project managers, trial designers and medical leads.

The value of this solution:

• Accelerated innovation.
• Greater productivity.

AI techniques used in this solution:

• LLMs enable researchers and protocol creators to more quickly develop materials and content.
• Small LLMs ensure protocol creators are using a limited, specific context window to templatize the protocol.

How AI helps:

• LLMs and small language models can be tailored in compliance with various regulations.
• Researchers can create, edit and update their protocols more quickly and with less human error.

The AI models provide:

• Large and small language models help condense the protocol development process, saving valuable time for creators.
• Large and small language models help populate templates and automate the creation of protocol components for greater efficiency.
• Large and small language models help fine-tune protocols to support regulatory compliance.

Create chatbots to engage with patients, sites, investigators and research teams more efficiently and effectively.

The value of this solution:

• Faster issue resolution.
• Improved customer service.
• Greater productivity.

AI techniques used in this solution:

• LLMs and natural language processing are used to train chatbots to effectively engage with patients and research teams.
• Intelligent decisioning helps increase engagement quality.

How AI helps:

• Optimize resources and improve engagement effectiveness.
• Increase patient, site and stakeholder satisfaction while maintaining data privacy.
• Be better prepared to respond quickly in times of disruption and uncertainty.

The AI models provide:

• GenAI models help life sciences organizations ensure the people they impact, including patients, sites and investigators, have better, more efficient support, regardless of operational hours.
• Chatbots can help identify stocking supply needs for certain prescriptions, identify the next best pharmacy to fill a prescription, and even provide information about adverse reactions and side effects to patients who might have questions about their medication.

Use chatbots powered by LLMs to optimize SKU-level warehouse inventory and dynamically adjust scenarios based on updated demand forecasts.

The value of this solution:

• Inventory optimization.
• Highly-accurate stock forecasting.
• Faster decision making.

AI techniques used in this solution:

• AI-driven machine learning models analyze vast amounts of data, recognize patterns and continuously adapt to provide accurate, automated stock forecasts and risk assessments.
• GenAI using small language models and natural language processing (NLP) enables efficient, human-like interactions by understanding user input, generating contextual responses and automating communication tasks in a resource-efficient manner.

How AI helps:

• Through insights generated by machine learning, the entire supply chain becomes more efficient as inventory levels are optimized based on data-driven predictions, reducing waste, cutting costs and improving overall performance.
• The models help optimize inventory levels by considering factors like lead times, storage costs, expiration dates and supplier reliability, ensuring that the right amount of stock is available when needed.

The AI models provide:

• Machine learning models analyze historical sales data, seasonal trends and external factors, like market demand and regulatory changes, to predict future inventory needs more accurately. This helps prevent stockouts or overstocking.
• Machine learning models learn and adapt over time based on new data inputs, improving forecasting accuracy as market conditions, product demand, or supply chain factors change.
• Models continuously monitor and assess risks, such as supply chain disruptions, changes in demand or supplier delays. They provide real-time, dynamic risk assessments, enabling proactive measures to mitigate issues.

Use NLP to automate the extraction and refinement of patient groups from large and varied data sets.

The value of this solution:

• More real-world evidence.
• Faster decision making.
• Greater productivity.

AI techniques used in this solution:

• NLP helps contextualize non-standard data formats to speed analysis.
• NLP accelerates the identification of patient characteristics.

How AI helps:

The ability to analyze diverse data sources and automate queries using NLP can significantly improve the efficiency and accuracy of cohort generation for clinical studies.

The AI models provide:

• NLP plays a pivotal role in patient cohort generation by automating data extraction, improving accuracy of cohort definitions and enabling the integration of diverse data sources.
• As the AI models advance, they will enhance research capabilities, streamline clinical trials and improve patient outcomes.

Understand how, why and when health care professionals (HCPs) engage across various sales and marketing channels. AI generates actionable insights that allow life science companies to personalize their interactions with HCPs more effectively, enhancing omnichannel engagement through data-driven decision making.

The value of this solution:

• Better customer experience.
• Greater customer engagement.
• Trustworthy insights.

AI techniques used in this solution:

• AI is used to process vast amounts of HCP engagement data across multiple channels.
• Advanced machine learning algorithms and predictive analytics uncover patterns and trends.
• AI enables personalization at scale by making real-time recommendations for optimized communication strategies.

How AI helps:

• AI helps life science companies gain a deeper understanding of HCP behavior across channels, leading to more meaningful insights about customer preferences and behaviors.
• AI supports personalization at scale, allowing companies to deliver personalized content and messages to HCPs based on their unique preferences and interaction history, which strengthens relationships and increases engagement effectiveness.
• By turning large volumes of data into actionable insights, AI helps companies make informed, data-driven decisions quickly, enabling faster adjustments in marketing strategies and sales approaches.

The AI models provide:

• AI facilitates seamless omnichannel integration and optimization, ensuring that interactions with HCPs are consistent, timely and relevant across platforms.
• Automating the analysis of engagement data and using AI for recommendations can reduce the manual workload on sales and marketing teams, allowing them to focus on higher-value tasks.
• With AI, companies can ensure they are targeting the right HCPs with appropriate messaging while also staying compliant with industry regulations.