Technology

The National Institute of Standards and Technology (NIST) has developed a comprehensive framework aimed at enhancing the trustworthiness of AI systems. This framework, known as the NIST AI Risk Management Framework (AI RMF), is designed to support stakeholders across industry, academia, and government in fostering responsible AI development and deployment. Core Components of the AI…

In today’s fast-paced world, effective time management and prioritization are crucial. Enter the Eisenhower Matrix, a powerful tool that can help you streamline your tasks and make better decisions. Named after President Dwight D. Eisenhower, this matrix categorizes tasks based on their urgency and importance, enabling you to focus on what truly matters. But did…

Implementing an AI pilot project for clinical documentation software requires meticulous planning and strategic execution. Here are the essential best practices for ensuring the success of your AI pilot project, from the initial planning stages to its full integration into existing systems. Formation of an AI Oversight Committee The journey begins with the formation of…

In the rapidly evolving field of artificial intelligence, NIST’s new Generative Artificial Intelligence (genAI) Profile guidance offers crucial strategies for managing AI risks. This post breaks down the essential tasks under four categories: Govern, Map, Measure, and Manage. Let’s delve into what each of these involves and how they can guide organizations in implementing genAI…

Introduction The National Institute of Standards and Technology (NIST) has crafted a robust framework aimed at guiding the responsible development and deployment of generative artificial intelligence. The draft Generative AI Intelligence Profile addresses critical concerns that span safety, security, ethical standards, and compliance issues. Today’s blog takes a deeper dive into this vital document, breaking…

Introduction In the rapidly evolving landscape of healthcare, artificial intelligence (AI) is transforming the way clinical documentation is managed. As biotech and pharmaceutical companies, academic institutions, and healthcare systems increasingly adopt AI tools to improve the accuracy and efficiency of clinical information, the need for robust governance structures becomes imperative. Drawing inspiration from proven, existing…

Toward the end of clinical document development, a quality control (QC) review is needed to verify data, check facts, ensure structure is internally logical and meets regulatory requirements, and proofread for typographical errors or inconsistencies. Implementing best practices allows the reviewer to optimize their review time and deliver valuable feedback, regardless of whether the working…