Welcome to Artificially Real, the only life sciences blog dedicated to AI solutions expanding Real World Data research.
Understand the biggest trends in AI impacting the Life Sciences industry. Understand the business values companies are recognizing through the use of LLMs and RWD.
Learn about changes companies have to make to achieve their business goals. Hear about the latest regulatory changes impacting your business operations.
Learn about industry best practices for designing, implementing, operating, and managing AI programs effectively.
Hear about critical regulatory, compliance, and privacy requirements as they change.
Learn how colleagues and peers how tackled similar challenges.
Learn about the innovative AI initiatives being undertaken by fellow Life Sciences firms.
Understand the business goals, processes, changes, and advancements undertaken to realize measurable value from the programs.
See the impacts realized by these inititives.
Read reviews of vendors that offer valuable components needed for effective RWD, AI, and analytics programs.
Learn about the capabilities, functionality, and focus of different vendors. Understand their developments and progress. We continually update our vendor listings to reflect the latest details.
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AI IN USE: Patient Journey Tracking
AI In Use: Patient Journey Tracking —
AI is transforming how life sciences executives understand and act on patient journeys. By integrating disparate real-world data (e.g. EHRs, claims, diagnostics, labs), AI can assemble complete timelines, revealing key intervention points, therapy switching patterns, and adherence risks. This article explores a five-stage pipeline, compliance considerations, and real-world use cases for optimizing therapy strategies, clinical trials, and patient engagement.
MCPs: THE AI BUILDING BLOCKS
Unraveling MCPs for Life Sciences
This article introduces Modular Component Pipelines (MCPs) as a foundational architecture for building scalable, transparent, and compliant LLM-powered systems in life sciences. Rather than relying on monolithic AI solutions, MCPs break down complex workflows into discrete, reusable components — such as data ingestion, normalization, prompt engineering, model routing, inference, validation, and feedback.
Key benefits include:
Agility to swap or update components independently
Transparency for regulatory compliance and quality assurance
Reusability across trials, therapeutic areas, or data types
The article highlights common MCP modules, shows how they enhance real-world data (RWD) workflows, and outlines high-impact use cases like patient timeline generation, trial matching, and literature surveillance. It makes the case that MCPs are essential to operationalize AI in life sciences — turning LLMs from experimental tools into enterprise-grade platforms.
Ario Health supports life sciences companies in designing and implementing MCP-based architectures to unlock the full potential of real-world data and generative AI.
BUILDING SMARTER LIFE SCIENCES LLMs: A Summary!
Article 6 – Series Summary
From Ario Health’s “Building Smarter Life Sciences LLMs” series
Summary – Bringing It All Together To Deploy AI
This summary article brings together the full Building Smarter Life Sciences LLMs series—offering life sciences IT leaders a practical roadmap to design, deploy, and govern large language models with confidence. Covering architecture, components, integration, lifecycle management, and compliance, it’s your strategic guide to turning LLM potential into real-world value.
BUILDING SMARTER LIFE SCIENCES LLMs: Ensuring Compliance and Data Privacy
Article 5 – Compliance & Data Privacy
From Ario Health’s “Building Smarter Life Sciences LLMs” series
As life sciences organizations scale large language models (LLMs), ensuring compliance and data privacy becomes a critical success factor.
Article 5 of Ario Health’s Building Smarter Life Sciences LLMs series focuses on how life sciences organizations can ensure compliance and data privacy when deploying large language models (LLMs).
The article outlines key strategies for embedding privacy-by-design, aligning with HIPAA, GDPR, and global regulations, monitoring for sensitive data leakage, implementing role-based access controls, and maintaining audit-ready documentation.
Packed with real-world use cases and expert tips, this guide helps IT leaders build LLM solutions that meet the highest standards for trust, transparency, and regulatory alignment—without slowing innovation.
Ideal for pharmaceutical, biotech, and healthcare AI teams looking to scale responsibly.
BUILDING SMARTER LIFE SCIENCES LLMs: Solution Training and Management
Article 4 – Solution Training and Management
From Ario Health’s “Building Smarter Life Sciences LLMs” series
Deploying an LLM is just the beginning. In life sciences, where precision and compliance are non-negotiable, managing that solution over time is critical. This article explores how to maintain performance, reliability, and regulatory alignment through structured LLM lifecycle management.
We dive into practical strategies for addressing prompt drift, implementing MLOps tailored to generative AI, monitoring output quality, enabling human-in-the-loop feedback, and establishing robust governance frameworks.
Each section includes real-world use cases and expert tips to help IT and strategy leaders ensure their LLMs continue delivering value long after go-live.
