TRIPODStatement

Reporting guidelines for clinical prediction (including for AI/ML)

TRIPOD+AI ( (

[TRIPODStatement @TRIPODStatement]

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

[TRIPODStatement @TRIPODStatement]

We are setting out to develop some new recommendations (TRIPOD-CODE) to provide guidance on reporting the availability & structure of code for predictive AI healthcare tools

Watch this space & read the protocol here

https://t.co/XKoXyBYzdY

#transparency #code #reproducibility

[TRIPODStatement @TRIPODStatement]

We are setting out to develop some new recommendations (TRIPOD-CODE) to provide guidance on reporting the availability & structure of code for predictive AI healthcare tools

Watch this space & read the protocol here

https://t.co/XKoXyBYzdY

#transparency #code #reproducibility

[TRIPODStatement @TRIPODStatement]

In TRIPOD+AI (https://t.co/hLbtcTmLdr) we ask authors to report the performance of their #AI model. This new authoritative position paper provides clarity on what measures should be reported and why

--> https://t.co/moEHH3sFCj

#predictiveAI #machinelearning #digitalhealth

[Xiaoxi @xiaoxi_6]

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 🙏🏼

[Bright Huo @brighthuo]

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/M66Bxrp5bt
👉https://t.co/B8G4Crz6SD
👉https://t.co/4cfOTDe7C5
👉https://t.co/ktoxV29H0b
👉https://t.co/TqT4r1xrHC
👉https://t.co/Uqh12Lkjti
E&E:
👉https://t.co/FJlehdpczF

Published in @BMJMedicine @BJSurgery @JAMANetworkOpen @BMCMedicine @AnnFamMed & Art Med
@GuyattGH | @GSCollins | @Cacciamani_MD | @CLaineMD | @AFlanagin | @thirumasu | @AlfonsoIorio | @juliomayol | @nliulab | @riazagha | @DrXiaoLiu | @SunjayaMD | @drnipunverma | @YJeremyNg | @mike_mitt | @meerpohl | @Tfeend | @eloder | @HelenFrankish | @LoebStacy | @tylermckechnie | @YungLeeMDMPH
@EQUATORNetwork

[Gary Collins @GSCollins]

NEW PAPER in @bmj_latest "Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide"

--> https://t.co/4YGRasQVrx

#methodologymatters

[Nan Liu, Duke-NUS @nliulab]

🌏 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

[Gary Collins @GSCollins]

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

[TRIPODStatement @TRIPODStatement]

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 👇

[Richard Riley (R²) @Richard_D_Riley]

**New Lancet DH paper

"Importance of sample size on the quality & utility of AI-based prediction models for healthcare"

- for broad audience
- why inadequate SS harms model training, evaluation & performance
- pushback to claims SS irrelevant to #AI
👇
https://t.co/FpxMsMP66A

[TRIPODStatement @TRIPODStatement]

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

[TRIPODStatement @TRIPODStatement]

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

[TRIPODStatement @TRIPODStatement]

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

[The BMJ @bmj_latest]

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

[TRIPODStatement @TRIPODStatement]

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

[TRIPODStatement @TRIPODStatement]

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

[TRIPODStatement @TRIPODStatement]

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

[The BMJ @bmj_latest]

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

[The BMJ @bmj_latest]

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