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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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.
