AI Powered SAMD

   Executive Summary Speed in Software as a Medical Device (SaMD) isn’t just about writing code faster. It’s about shipping the right, validated product —with traceability, clinical evidence, and regulatory documentation ready on day one. Akra.ai accelerates this journey by providing SaaS application infrastructure and built-in compliance automation that turn scattered activities—requirements, risk, verification, validation, clinical evaluation, cybersecurity, and post-market monitoring—into a single, auditable delivery pipeline . The result: shorter cycles , lower validation effort , and cleaner submissions —without compromising safety or quality. SaMD Companies Choose Akra.ai for Faster Time-to-Market The Problem SaMD Teams Face (and why time slips) Fragmented toolchains: Jira + spreadsheets + shared drives = broken traceability and manual document assembly. Late compliance: Teams “bolt on” ISO 14971, IEC 62304, and 21 CFR Part 11 after build—creating rework ...

  Artificial Intelligence (AI) is transforming healthcare — from diagnostic imaging and predictive analytics to personalized treatment plans and remote patient monitoring. But in a regulated industry like healthcare, accuracy alone isn’t enough . AI algorithms in medical software must be validated to ensure they are safe, effective, reliable, and compliant with stringent regulatory requirements. This article will serve as a comprehensive guide to the best practices for validating AI algorithms in medical software — covering data, methodology, regulatory frameworks, performance metrics, and ongoing monitoring . AI Algorithms in Medical Software 1. Why Validation Is Crucial in Medical AI Validation ensures that an AI model: Performs accurately in real-world clinical settings Generalizes across different patient populations and clinical environments Complies with regulatory standards like FDA, EMA, or MHRA guidelines Protects patient safety by mitigating algorithmic b...

  Introduction As healthcare and life sciences industries adopt increasingly complex software solutions—especially Software as a Medical Device (SaMD) and clinical research platforms—the need for effective, compliant validation processes becomes critical. Two dominant approaches have emerged: traditional manual validation processes and cloud-based validation tools . While both aim to ensure regulatory compliance (e.g., FDA 21 CFR Part 11, GxP, ISO 13485), they differ significantly in execution, scalability, risk management, and audit readiness. Manual Validation Processes: Traditional But Tiring Manual validation involves a paper-based or document-heavy approach that requires test scripts, validation reports, spreadsheets, and manual traceability matrices. Advantages: Familiar and widely used by legacy teams. Full control over each step of the validation process. Doesn’t require sophisticated infrastructure or training. Disadvantages: Time-consuming and prone ...