sensemaking360

  Leveraging the latest GPT models like GPT-4 for building a Digital AI Assistant to interpret X-ray and MRI images involves a multifaceted approach, integrating advanced AI technologies with healthcare systems. Here's a conceptual framework for such a project: Medical Image Interpretation : Integration with Medical Imaging Technologies : The AI assistant needs to be integrated with medical imaging technologies like X-ray, MRI, and CT scan equipment. This integration enables the AI to access and interpret medical images directly. Training on Medical Imaging Datasets : GPT-4 or similar models should be supplemented with specialized AI algorithms trained on vast datasets of medical images. This training would involve collaboration with medical institutions to access diverse and anonymized datasets. Educational Interface for Patients : Simplified Explanations : The AI can provide easy-to-understand explanations of medical images, helping patients grasp their medical conditions better....

  Remaining competitive in an era of unprecedented change requires organizations to become adept at rapidly adapting to new realities. Leaders play a key role in building adaptive capacity through three interconnected capabilities: sensemaking, causal reasoning, and experimentation. Sensemaking involves developing useful interpretations of ambiguous situations to support action. It entails gathering inputs from diverse sources, uncovering biases, and creating evolving maps or models of the environment. Sensemaking is social and benefits from collective inquiry. Sensemaking grew out of organizational theory in the 1970s-1980s, with foundational work by Karl Weick examining how people make retrospective meaning amidst ambiguity. 1990s - Weick and others continued to develop sensemaking as making the unfamiliar familiar through labels, concepts, and actions. Sensemaking was seen as an organizing process. 2000s - Research delved into dynamics of collective/social sensemaking, power in...

 ### Introduction Systems thinking is a powerful tool for addressing complex problems and improving the world around us. However, there is often a gap between the promise and practice of systems thinking. This blog post will discuss how causal modeling can help to address this gap. ### What is causal modeling? Causal modeling is a type of systems thinking that focuses on understanding and quantifying the causal relationships between variables in a system. Causal models can be used to simulate the effects of different interventions, to identify the root causes of problems, and to communicate complex ideas in a clear and concise way. ### How can causal modeling help to address the gap between thinking and practice? Here are some specific ways in which causal modeling can help to address the gap between thinking and practice: * **Improved understanding of complex systems:** By identifying and quantifying the causal relationships between variables, causal modeling can help us to better...