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- Hospitals tackle AI validation 🏥, Runway’s camera control update 🎥, Claude 3.5 reads PDFs 📄
Hospitals tackle AI validation 🏥, Runway’s camera control update 🎥, Claude 3.5 reads PDFs 📄
AI For Work
November 05, 2024
Welcome, innovators.
Health systems face mounting pressure to adopt new AI tools, but doing so is easier said than done. That’s because there’s no identified system for measuring the effectiveness of this technology.
Below, we talk about where things are heading from here.
In today’s newsletter:
Today’s must-read stories 📰
Hospitals assess strategies to make AI effective
Prompt of the day ✍️
Together with MindStudio
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Today’s must-read stories
Runway announces Advanced Camera Control 📷
The Claude 3.5 Sonnet model now supports PDF input, interpreting both text and visuals in documents.
AI Video of the Day 📽️
Miscellaneous resources
OpenAI X account: Updates on AI research and advancements, including their flagship AI models like ChatGPT.
Google AI X account: Cutting-edge AI developments from Google, highlighting projects and real-world applications.
AI at Meta X account: Meta’s innovations in AI technology, with a focus on open research and machine learning breakthroughs.
AI tools 🔨
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Hospitals assess strategies to make AI effective
Hospitals are facing growing pressure to implement AI, but without standardized ways to assess its effectiveness, risks remain high. A coalition of health systems and tech experts is working to change that by creating an AI validation framework tailored to healthcare.
Key takeaways
Complexity of AI explanations: Even AI developers often struggle to explain how a model arrives at its predictions, posing safety risks in clinical settings.
Coalition for health AI: A network of "assurance labs" aims to validate and monitor AI systems, ensuring they are safe and unbiased.
Standardization efforts: New "ingredient and nutrition labels" will provide transparent evaluation criteria for AI models, testing them on representative patient data.
Unique challenges for hospitals: Unlike traditional medical devices, AI models require continuous monitoring since they evolve and vary with different data inputs.
Resource limitations: Larger health systems may have governance committees for vetting AI, but smaller facilities lack the resources for ongoing validation.
Our thoughts
As AI technology floods healthcare, hospitals must take a careful, systematic approach to validate its effectiveness and reliability. The Coalition for Health AI’s efforts could provide much-needed structure, helping hospitals make safer, data-driven decisions that prioritize patient outcomes.
Prompt of the day ✍️
HRIS specialist (create an HR metrics report)
How it works:
This prompt guides the creation of an HR metrics report by asking questions to gather details, incorporating reference materials, evaluating the work against a rubric, and refining the report based on feedback and set rules.
Why use it:
This prompt creates a personalized, high-quality HR metrics report by using expert evaluations and tailored insights, allowing HR professionals to save time and make more accurate decisions.
Prompt Example:
{"prompt":"Develop a tailored Meeting Minutes aligned with the user's individual nprompt":"Develop a tailored HR Metrics Report aligned with the user's individual needs, drawing insights from the supplied reference materials. Initiate interaction with the user to obtain essential specifics and resolve any ambiguities. Iteratively refine the HR Metrics Report through consistent evaluations using the given evaluationRubric and gather user input to ensure the end product aligns with the users expectations.}You can find the prompt in its entirety here.
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