Spend :01 of your time each Monday morning as Twelve:01 delivers timely tools, trends, strategies, and/or compliance insights for the CME/CE enterprise.
Claude Cowork, part of Anthropic’s paid platform, is an “agentic” workspace that can execute multi-step tasks across files and tools rather than simply respond to prompts. These tools can organize projects and break work into structured steps, generating outputs such as reports and presentations while maintaining context within a shared workspace. It also incorporates permission-based access controls, ensuring it only interacts with files and folders explicitly allowed while operating in a contained environment. For CME/CE professionals, this emerging category of tools may offer new efficiencies in research synthesis, content development, and project management. At the same time, their use raises important considerations around oversight, data governance and how AI-generated outputs are validated before utilization.
Joint Accreditation (JA) permits organizations to collaborate in joint providership as long as all partners are “eligible companies,” meaning they are not considered ineligible under the Standards for Integrity and Independence. These collaborations can occur across several configurations, including partnerships with other JA-accredited providers, with an organization holding individual CME/CE accreditations, or with an organization that holds no CME/CE accreditation at all. In every scenario, one JA-accredited provider must be designated as the primary provider responsible for ensuring compliance with JA criteria, policies, and standards. Notably, JA requires use of its joint providership statement across all scenarios, reinforcing transparency and standardization in how collaborative educational activities are communicated.
Recent PubMed authors argue that artificial intelligence (AI), particularly large language models, is poised to reshape graduate and continuing medical education (CME/CE) by enabling more personalized, data-driven, and in-workflow learning. They highlight the rise of “precision education,” an already familiar term, outlining how AI will adapt to individual learner needs, deliver automated feedback, and more tightly link education to clinical decision-making and performance. At the same time, they caution about risks such as bias, hallucinations, privacy concerns, and overreliance on AI could undermine clinical reasoning if not carefully managed. The authors emphasize the need for stronger AI literacy, clear ethical guardrails, and rigorous evaluation of emerging tools. For CME/CE providers, this underscores both risk and opportunity as the role shifts from content producer to designer and steward of AI-enabled learning ecosystems aligned with competency-based education and patient outcomes.
