We are setting out to develop some new recommendations (TRIPOD-CODE) to provide guidance on reporting the availability and structure of code for predictive AI healthcare tools Watch this space, and read the protocol here link.springer.com/article/10.1... #transparency #code #reproducibility

We are setting out to develop some new recommendations (TRIPOD-CODE) to provide guidance on reporting the availability and structure of code for predictive AI healthcare tools Watch this space, and read the protocol here link.springer.com/article/10.1... #transparency #code #reproducibility

Looking to assess adherence to TRIPOD+AI? We have a new #openaccess paper in @jclinepi.bsky.social "Adherence to TRIPOD+AI guideline: an updated reporting assessment tool" --> linkinghub.elsevier.com/retrieve/pii... #machinelearning #AI #metascience #transparency

End of year reminder: the TRIPOD+AI reporting guideline is reporting standard for all clinical prediction model studies, including those using machine learning and AI. --> www.bmj.com/content/385/...

"The making of a statistician: Doug Altman" - just published in @bmj.com celebrating his 1 million citations and reflecting on his remarkable career and legacy. One of the most influential statisticians in modern medical research. --> www.bmj.com/content/391/... #BMJChristmas #methodologymatters

In TRIPOD+AI (tinyurl.com/4s6pmz5d) we ask authors to report the performance of their #AI model, this new authoritative position paper provides clarity on what measures should (and should not) be reported and why --> tinyurl.com/bdehp3ht #predictiveAI #machinelearning #digitalhealth #transparency

In TRIPOD+AI (tinyurl.com/4s6pmz5d) we ask authors to report the performance of their #AI model, this new authoritative position paper provides clarity on what measures should (and should not) be reported and why --> tinyurl.com/bdehp3ht #predictiveAI #machinelearning #digitalhealth #transparency

NEW PAPER: "Reporting guidelines for studies involving generative artificial intelligence applications: what do I use, and when?" --> www.nature.com/articles/s41... #HealthTech #ClinicalAI #MachineLearning #MedAI #AIinMedicine #TransparentAI #HealthInnovation #GenAI

NEW PAPER "The STARD-AI reporting guideline for diagnostic accuracy studies using #artificialintelligence" --> www.nature.com/articles/s41... #machinelearning #MLsky #statssky #digitalhealth #transparency

NEW PAPER in @bmj.com "Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide" --> www.bmj.com/content/390/... #methodologymatters #StatsSky #EpiSky

NEW PREPRINT "Critical Appraisal of Fairness Metrics in Clinical Predictive AI" -> arxiv.org/abs/2506.17035 We identified 62 fairness metrics (and growing) - unsurprisingly it's all a bit of a mess...with most metrics not fit for purpose #predictiveAI #fairness #machinelearning #StatsSky #MLSky

Maternal early warning scores shown to be methodologically weak and at high risk of bias - Journal of Clinical Epidemiology www.jclinepi.com/article/S089...

Item 10 of the TRIPOD+AI asks (www.bmj.com/content/385/...) "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)@richarddriley.bsky.social · last yr.

**New Lancet DH paper** "Importance of sample size on the quality & utility of AI-based prediction models for healthcare" - for broad audience - explains why inadequate SS harms #AI model training, evaluation & performance - pushback to claims SS irrelevant to AI research 👇 tinyurl.com/yrje52fn

**New Lancet DH paper** "Importance of sample size on the quality & utility of AI-based prediction models for healthcare" - for broad audience - explains why inadequate SS harms #AI model training, evaluation & performance - pushback to claims SS irrelevant to AI research 👇 tinyurl.com/yrje52fn

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

Rigorous study design and analytical standards are required to generate reliable findings in healthcare from artificial intelligence (AI) research. On…

sciencedirect.com

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: bmj.com/content/385/... #HealthTech

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studie...

bmj.com

Complete and transparent reporting aids critical and assessing risk of bias in #predictiveAI. TRIPOD+AI and PROBAST+AI are key tools to improve #AI research in healthcare TRIPOD+AI -> tinyurl.com/39pz3rfd PROBAST+AI -> tinyurl.com/yt8vrvrf #Digitalhealth #healthcareAI #machinelearning

Early-career researchers and students in health research! Join the UK EQUATOR Centre's Publication School to learn how to plan, write, and publish excellent journal articles. Date: Mondays, 23 June – 14 July 2025 Time: 9:30 – 13:30 GMT+1 Where: Online via Zoom bit.ly/equatorpubsc...

UK EQUATOR Centre Publication School

Fit for purpose: The secrets of success in writing, publishing, and disseminating research articles

bit.ly

Health researchers, have you ever wondered how to: • Negotiate authorship • Choose a journal • Develop good writing habits • Write each section of an article • Revise your own writing • Respond to peer review The UK EQUATOR Centre's Publication School is here to help! bit.ly/equatorpubsc...

UK EQUATOR Centre Publication School

Fit for purpose: The secrets of success in writing, publishing, and disseminating research articles

bit.ly

The new PROBAST+AI tool to assess quality & risk of bias of #predictive#AI models in healthcare is predicated on good reporting, i.e., by following the TRIPOD+AI guidance PROBAST+AI www.bmj.com/content/388/... TRIPOD+AI www.bmj.com/content/385/... #MLSky #StatsSky #digitalhealth #machinelearning

*NEW PAPER* PROBAST+AI: an updated quality, risk of bias & applicability assessment tool for prediction models using regression or AI methods PROBAST+AI consists of two distinct parts: - model development (quality assessment tool) - model evaluation (risk of bias tool) www.bmj.com/content/388/...

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

The Prediction model Risk Of Bias ASsessment Tool (PROBAST) is used to assess the quality, risk of bias, and applicability of prediction models or algorithms and of prediction model/algorithm studies....

bmj.com

NEW PAPER in the @bmj.com "PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or #artificialintelligence methods" www.bmj.com/content/388/... #StatsSky #MLSky #AI #MethodologyMatters

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 😁 www.bmj.com/content/385/... #MLsky #StatsSky #MedSky #transparency #AI

[ #Webinar ] D-8 until our next webinar! ⤵️ 💬The importance of transparency in predictive AI: the role of reporting guidelines 🗣️ @gscollins.bsky.social (@ox.ac.uk) 💻https://sesstim.univ-amu.fr/fr/content/webinar-quantim-gary-collins

TRIPOD+AI (Expanded E&E): "If uncertainty intervals for individual prediction model outputs have been presented then provide details on how this was done" (www.bmj.com/content/385/...) 👇This new paper provides insight into uncertainty of risk estimate on decision making www.bmj.com/content/388/...

TRIPOD+AI (Expanded E&E): "If uncertainty intervals for individual prediction model outputs have been presented then provide details on how this was done" (www.bmj.com/content/385/...) 👇This new paper provides insight into uncertainty of risk estimate on decision making www.bmj.com/content/388/...

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare www.bmj.com/content/388/... #artificialintelligence #healthcare #bioinformatics #ethics #trust

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI f...

bmj.com

Underpinning the FUTURE-AI recommendations 👇 is transparency Reporting guidelines like TRIPOD+AI are essential to ensure all key details are completely & transparently reported Get them here -> www.bmj.com/content/385/... #predictiveAI #machinelearning #trustworthyAI #healthcareAI #digitalhealth

Gary Collins@gscollins.bsky.social · last yr.

NEW PAPER in @bmj.com "FUTURE-AI: international consensus guideline for trustworthy and deployable #artificialintelligence in healthcare" from design, development, validation to regulation, deployment & monitoring www.bmj.com/content/388/... #StatsSky #MLSky #MedSky

NEW PAPER in @bmj.com "FUTURE-AI: international consensus guideline for trustworthy and deployable #artificialintelligence in healthcare" from design, development, validation to regulation, deployment & monitoring www.bmj.com/content/388/... #StatsSky #MLSky #MedSky

👇 Item 10 of TRIPOD+AI "Explain how the study size was arrived at (separately for development and evaluation), and justify that the study size was sufficient to answer the research question. Include details of any sample size calculation" www.bmj.com/content/385/... #MLSky #StatsSky

Journal of Clinical Epidemiology@jclinepi.bsky.social · 2y ago

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review - Journal of Clinical Epidemiology www.jclinepi.com/article/S089...

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 😁 www.bmj.com/content/385/... #MLsky #StatsSky #MedSky #transparency #AI