Atlas
AI can help generate procedures and Root Cause Analysis (RCA) reports, but AI-generated documents are not automatically FDA-compliant. Compliance depends on system design, data provenance, human oversight, and validation.
Atlas
Yes, LLM-based quality systems can pass an FDA Part 11 audit, but only with validation of intended use, controlled inputs and outputs, ALCOA+ data integrity, audit trails, rigorous change control, and human oversight. This guide walks through the controls and artifacts you need.
Atlas
AI-driven inspection models will enable FDA audits to be faster, more data-driven, and more focused on high-risk signals.
Atlas
The FDA's expectations for SaMD (Software as a Medical Device) submissions have shifted to a total product life cycle (TPLC) model emphasizing risk, data governance, human factors, and post-market monitoring. This guide breaks down what reviewers now expect, especially for AI/ML-enabled devices.
Atlas
The FDA's 2025 AI/ML draft guidances shift from exploratory policy to operational expectations. Key changes include a risk-based credibility framework, lifecycle recommendations for AI-enabled device software, stronger data quality and bias requirements, and formalized predetermined change control plans.
Atlas
Model Context Protocol gives AI agents a standardized way to reach ELNs, LIMS, QMS, and regulatory systems. It also introduces prompt injection, IP exfiltration, over-privileged delegation, and GxP audit-trail gaps that pharma and medical device teams must design around from day one.
Atlas
The Model Context Protocol (MCP) is becoming the standard layer that lets LLMs and agentic AI interact safely with enterprise systems. This post covers the trends shaping MCP adoption in pharma, from governance and federated identity to regulator-ready audit trails and secure model orchestration.
Atlas
The Model Context Protocol (MCP) is a new standard that allows AI models to connect easily with data, systems, and tools.
Atlas
The Model Context Protocol (MCP) is an emerging open standard that lets large generative models connect securely with external data sources, tools, and workflows. In drug design, it helps AI access lab data and structure libraries in a standardized, auditable way, though full readiness still depends on validation and governance.
Atlas
Yes. MCP (Model Context Protocol) can build a standardized data layer that bridges silos across sponsors, CROs, and regulators in multinational trials.
Atlas
MCP (Model Context Protocol) is emerging as the backbone of AI and digital twin integration in pharma and medical devices. It links near-real-time equipment and quality data to AI models for predictive maintenance, process optimization, and QC.
Atlas
Artificial intelligence has already improved how research is carried out in pharma and medical devices, but the real challenge comes when we try to scale these models beyond the lab and into commercial manufacturing. MCP offers a standardized way to bridge that gap.
Atlas
MCP can support near-real-time decision-making across global R&D hubs without compromising compliance, but only with secure architecture, strong governance, and risk-based regulatory strategy. This post covers the blueprint, risks, and operational benefits.
Atlas
MCP cuts human error in clinical data management and regulatory reporting. It standardizes data interactions, automates processing, enables near-real-time validation, and creates detailed audit trails, so teams ship faster with fewer mistakes.
Atlas
Data silos slow pharma and medical device R&D, causing inefficiencies and errors. MCP enables AI systems to access and integrate data across departments securely, streamlining workflows and accelerating decision-making.