NEW PAPER out today in @BMJ_latest TRIPOD+AI: reporting recommendations for studies developing or validating prediction models for use in healthcare that use #machinelearning methods https://t.co/ed9oiB1MVG #ArtificialIntelligence #AIstandards #OpenAccess Please share 🙏

NEW paper "Clinical prediction models using machine learning in oncology: challenges and recommendations" --> https://t.co/n9uOpE9A0D #machinelearning #methodsmatter #digitalhealth

Clinical prediction models are widely developed in the field of oncology, providing individualised risk estimates to aid diagnosis and prognosis. But they suffer from common limitations like handling missing data, addressing demographic fairness and managing complex data.

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 🙏🏼

NEW PAPER "The STARD-AI reporting guideline for diagnostic accuracy studies using #artificialintelligence" in @NatureMedicine --> https://t.co/7IGGyF4MPK #machinelearning #digitalhealth #transparency

*NEW PAPER* "Guidelines for Meta-analyses and Systematic Reviews in Urology" - Led by @VickersBiostats - Addresses common shortcomings of meta-analysis articles - Provides guidelines for improvement - Applicable to all fields, not just urology 🙏 https://t.co/40wuhFTNkD

What is the #CHART reporting guideline designed to improve in chatbot health-advice studies? 📌 Key Findings -Systematic review revealed wide variability in reporting practices across chatbot health advice studies. -Developed a draft checklist refined via Delphi consensus with

[via @uroAI_news] Guideline Sheds Light on Best Practices for Reporting AI-Powered Health Chatbot Studies https://t.co/ivEJPMmtfr Kudos to @brighthuo and @GuyattGH for leading this incredible effort 👏 Contributors & Collaborators Special thanks to the amazing team who made

Introducing #CHART - reporting guidelines for studies evaluating evidence summaries and health advice from #genAI models like #ChatGPT and other #LLMs Developed with input from 531 international, multidisciplinary stakeholders Authors may apply the #CHART checklist ✅and

Chatbot Studies Guideline: CHART 📑 12 items, 39 subitems for clear CHA study reporting 🌐 531 stakeholders in consensus process 📊 Improves transparency & comprehension #AI #MedicalAI #Research #CHART #Pub2Post https://t.co/aCgktz8LFN

Developed by an international team of clinicians, researchers, and journal editors, the Chatbot Assessment Reporting Tool (CHART) is a guideline that includes 12 items and 39 subitems developed to provide reporting recommendations for studies evaluating the performance of

Take part in our survey and help shape how trial results are shared with patients & the public. 🔊 Final round open! 📝 Just 11 items to rate ⏱️ ~10 mins 📢 Open to researchers, patients and the public Take part here: https://t.co/ow7EPBwNbU #PPI #Trials #HealthResearch

NEW PAPER: the CHART reporting guideline for studies evaluating generative AI models like #ChatGPT & other #LLMs for providing health advice https://t.co/M8cxzBm60K The explanation & elaboration paper https://t.co/tBwkxnq13b #genAI #chatbots #healthadvice #reporting

This reporting guideline offers advice on how to report studies that evaluate the performance of generative AI-driven chatbots for the purposes of summarising clinical evidence and providing health advice https://t.co/grAnDfTfDI

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 (Research Methods & Reporting): “Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide” led by Pedro Lopez-Ayala & Tobias Zimmermann with @GSCollins & myself https://t.co/SAdnUoAHrf

This article provides an overview of the current state of handling continuous variables in healthcare research. It discusses the potential limitations of assuming a linear relationship between independent and dependent variables https://t.co/i7i8xOcEg4

Thrilled to see our work published in @bmj_latest. A practical guide on why categorizing continuous variables or assuming linearity often fails and how to model non-linear relationships using splines. Code for Case Study available on GitHub! @Richard_D_Riley @GSCollins

Outstanding paper! Especially suited for non-statisticians who want to get started with relaxing linearity assumptions without resorting to dreaded categorization techniques ... @Statistics

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

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