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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BUILDING SMARTER LIFE SCIENCES LLMs: Ensuring Compliance and Data Privacy
Article 5 – Compliance & Data Privacy
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.
BUILDING LIFE SCIENCES LLMs: Data Access and Integration
LLMs are only as good as the data they’re built on.
In Article 3 of Ario Health’s Building Smarter with LLMs series, we dig into one of the most overlooked—but mission-critical—elements of any life sciences AI initiative: Data Access and Integration.
From handwritten clinical notes to fragmented claims databases, real-world data is messy, multi-modal, and inconsistent. This article lays out how to turn that complexity into a competitive advantage by:
Connecting siloed systems
Normalizing and aligning clinical semantics
Structuring unstructured text for LLM-readiness
Enabling full traceability and data lineage
Automating secure, role-based data pipelines
Whether you’re enabling safety signal detection, clinical trial optimization, or medical information workflows, clean, connected data is your foundation for safe and scalable LLMs.
BUILDING LIFE SCIENCES LLMs: Essential Solution Components
In Article 2 of Ario Health’s “Building Smarter with LLMs” series, we go under the hood to explore the six essential solution components that turn LLM architecture into real enterprise intelligence.
From prompt orchestration to retrieval-augmented generation (RAG), biomedical embeddings, and human-in-the-loop feedback—this guide breaks down the infrastructure needed to support scalable, compliant, and high-impact AI systems.
🧬 Designed for life sciences. Built for real-world data.
#LLMs #AIinHealthcare #RWD #LifeSciencesIT #DigitalBiotech #GenerativeAI #ArioHealth
BUILDING LIFE SCIENCES LLMs: Critical Design and Architecture Decisions
LLMs in Life Sciences: It All Starts with Architecture
In the race to turn Real World Data (RWD) into actionable insights, Large Language Models (LLMs) offer a quantum leap forward. But just like drug discovery, success starts with the very first processes in early development to create a robust solution. For LLMs to deliver tangible value—whether for accelerating evidence generation, powering decentralized trials, or streamlining regulatory submissions—they need more than great algorithms.
It begins with great architecture!
Before you fine-tune a model or write your first prompt, you need a strategy—and that begins with technical architecture.
In this first article in our five-part “Building Life Sciences LLMs” series, we dive into the foundational decisions that shape scalable, secure, and compliant LLM solutions for analyzing Real World Data (RWD) in life sciences.
What you’ll learn:
When to choose centralized, federated, or hybrid models
How to modularize components for faster scaling
What infrastructure fits your privacy and performance needs
How to embed HIPAA and GDPR compliance from the start
How to align LLMs with real business outcomes across R&D, regulatory, and commercial teams
📈 Whether you’re building for scientific discovery, patient safety, or field enablement—your LLM success depends on a solid foundation.
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