Thank you @Anaes_Journal for the incredible opportunity to share our work! Grateful to the GRAITE-USRA steering group, all Delphi experts & endorsing societies for their invaluable contributions, insights & support. A true multidisciplinary effort to improve AI reporting in RA 🙏🏼

Attention #AI researchers, clinicians, patients, editors, publishers, & beyond👋 Just in 🖨️ - the #CHART reporting guideline ✅ for studies evaluating #genAI models like #ChatGPT & other #LLMs for health advice Statement: 👉 https://t.co/IRZ8SCKBrz 👉 https://t.co/4MiQsHHRxm

NEW PAPER in @bmj_latest "Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide" --> https://t.co/4YGRasQVrx #methodologymatters

🌏 Advancing international partnership for governing generative #AI (#GenAI) models in #medicine and #healthcare. We introduce POLARIS-GM initiative: a scenario-based, consensus-driven framework for GenAI #governance and #regulation. @NatureMedicine https://t.co/tEKa6khPqB

NEW PREPRINT "Critical Appraisal of Fairness Metrics in Clinical Predictive AI" -> https://t.co/TpmpXSrJN6 We identified 62 fairness metrics (& growing) - unsurprisingly it's all a bit of a mess...with most metrics not fit for purpose #predictiveAI #fairness #machinelearning

Item 10 of the TRIPOD+AI asks ( https://t.co/BXjSKKytRx) "Explain how the study size was arrived at, and justify that the study size was sufficient to answer the research question. Include details of any sample size calculation" Here's why it's important 👇

Let’s raise the standard! Adopting TRIPOD+AI means advancing equitable, accountable AI ready for clinics. Check guidelines: https://t.co/sGcNFspAto 📖 Together, we can ensure AI serves patients first. #OpenScience #AIforGood

Who uses TRIPOD-AI? 👩🔬 Researchers designing models 👨⚕️ Clinicians evaluating tools 👩💻 Developers building algorithms 📋 Journals & peer reviewers A shared framework for responsible innovation. 🤝 #DigitalHealth #HealthcareAI

Why TRIPOD-AI? 🔹 Ensures models can be replicated/validated 🔹 Builds trust with clinicians & patients 🔹 Reduces bias risks in outcomes 🔹 Bridges code to real-world care Better reporting = better science for healthcare challenges. 🌍💡 #AIethics #MedEd

TRIPOD-AI’s pillars: ✅ Transparent data sources & preprocessing ✅ Full model architecture/training details ✅ Rigorous validation (internal/external) ✅ Ethics checks & bias mitigation ✅ Clear clinical impact No more “black box” AI! 🔍 #EthicalAI #MachineLearning

A guideline to boost transparency in AI-driven medical prediction models! Evolved from TRIPOD, it ensures studies are reproducible, ethical, and clinically meaningful. Crucial for trustworthy #AI in healthcare. Read the paper: https://t.co/BXjSKKytRx #HealthTech

Reporting guidelines have become an essential instrument of scientific integrity. We need to make the leap from just producing reporting guidelines to helping researchers put them into practice, writes @GSCollins https://t.co/6gGm860sUk

A periodic reminder that if you are writing up your study developing/validating a #machinelearning clinical prediction model then make sure you are reporting all the necessary information by following the TRIPOD+AI standards 🙏 https://t.co/8FfVBrKnGp #transparency #AI

Underpinning the FUTURE-AI recommendations 👇 is transparency TRIPOD+AI recommendations are essential to ensure all key details are completely & transparently reported https://t.co/BXjSKKytRx #predictiveAI #machinelearning #trustworthyAI #healthcareAI #digitalhealth

Underpinning the FUTURE-AI recommendations 👇 is transparency TRIPOD+AI recommendations are essential to ensure all key details are completely & transparently reported https://t.co/BXjSKKytRx #predictiveAI #machinelearning #trustworthyAI #healthcareAI #digitalhealth

This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare, @KarimLekadir and colleagues https://t.co/VMOdLpa88g

A periodic reminder that if you are writing up your study developing/validating a #machinelearning clinical prediction model then make sure you are reporting all the necessary information by following the TRIPOD+AI standards 🙏 https://t.co/8FfVBrKnGp #transparency #AI

"Advances in LLMs have stretched regulatory structures to their limits, exposing a patchwork of solutions that do not fully encompass the nuances of these models." New @EQUATORNetwork guideline for biomedical applications of LLMs by @dbittermanmd et al:

10/ 🌐 Learn more and access the guideline here: https://t.co/VP3hjdpWpD Together, we can ensure AI advances responsibly, improving healthcare outcomes while minimizing risks. Let’s build a fair, transparent AI future! 🌟 #AI4Good

1/ 🌟 Exciting news for healthcare AI! The TRIPOD-LLM guideline sets new standards for reporting studies involving large language models (LLMs) in healthcare. A game-changer for transparency and reproducibility. Here’s a deep dive! 🧵👇 #AIinHealthcare #LLMs