Maarten van Smeden

Vacancy — PhD position on AI methodology for prediction of patient outcomes using organoid models Are you passionate about bringing personalized medicine to the next level and make real impact in healthcare? Join our team and develop novel AI methodology to improve predictions of relevant patient ...

NEW FULLY FUNDED PHD POSITION

Looking for a motivated PhD candidate to join our team. Together with Danya Muilwijk, Jeffrey Beekman and I, you will explore opportunities and limitations of AI in the context of organoids

For more info and for applying 👉
www.careersatumcutrecht.com/vacancies/sc...

Interpretable "AI" is just a distraction from safe and useful "AI"

This is right tho. Let’s therefore call them sensitivity positive predictive value curves bsky.app/profile/laur...

Performance evaluation of predictive AI models to support medical decisions: Overview and guidance A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predi...

For details: arxiv.org/abs/2412.10288

No.

I wonder who those people are who come here dying to know what GenAI has done with some prompt you put in

If you think AI is cool, wait until you learn about regression analysis

TL;DR: Explainable AI models often don't do a good job explaining. They can be very useful for description. We should be really careful when using Explainable AI in clinical decision making, and even when judging face validity of AI models

Excellently led by @alcarriero.bsky.social

NEW PREPRINT

Explainable AI refers to an extremely popular group of approaches that aim to open "black box" AI models. But what can we see when we open the black AI box? We use Galit Shmueli's framework (to describe, predict or explain) to evaluate

arxiv.org/abs/2508.05753

Guidelines for Reporting Observational Research in Urology: The Importance of Clear Reference to Causality - PubMed Observational studies often dance around the issue of causality. We propose guidelines to ensure that papers refer to whether or not the study aim is to investigate causality, and suggest language to ...

This is, however, not clever or safe writing, it is a bad collective habit that needs to stop. Not by avoiding references to causality but by clear referencing to it

pubmed.ncbi.nlm.nih.gov/37286459/

The healthcare literature is filled with "risk factors". This word combination makes research findings sound important by implying causality, while avoiding direct claims of having identified causal associations that are easily critiqued.

And taking this analogy one step further: it gives genuine phone repair shops a bad name

When forced to make a choice, my choice will be logistic regression model over linear probability model 103% of the time

Cover picture with blog title & subtitle, and results graph in the background

Post just up: Is multiple imputation making up information?

tldr: no.

Includes a cheeky simulation study to demonstrate the point.
open.substack.com/pub/tpmorris...

The leaky pipe of clinical prediction models. by @maartenvsmeden.bsky.social‬ et al

You can have all the omni-omics data in the world and the bestest algorithms, but eventually a predicted probability is produced & it should be evaluated using well-established methods, and correctly implemented in the context of medical decision making.

statsepi.substack.com/i/140315566/...

Clients: “I want to find real, meaningful clusters”
Me: “I want world peace, which is more likely to happen than what you want”

Depending which methods guru you ask every analytical task is “essentially” a missing data problem, a causal inference problem, a Bayesian problem, a regression problem or a machine learning problem

In medicine they are called "risk factors" and, of course, you want all "important" risk factors in your model all the time

Unless a risk factor is not statistically significant then you can drop that factor without issues

* New preprint led by Joao Matos & @gscollins.bsky.social

"Critical Appraisal of Fairness Metrics in Clinical Predictive AI"

- Important, rapidly growing area
- But confusion exists
- 62 fairness metrics identified so far
- Better standards & metrics needed for healthcare
arxiv.org/abs/2506.17035

Also, the fact that a model with the best AUC doesn't always mean the model makes the best predictions is lost in such cases too

Surprisingly common thing: comparisons of prediction models developed using, say, Logistic Regression, Random Forest and XGBoost with conclusion XGBoost is "good" because it yields slightly higher AUC than LR or RF using the same data

Fact that "better" doesn't always mean "good" seems lost

Published: the paper 'On the uses and abuses of Regression Models: a Call for Reform of Statistical Practice and Teaching' by John Carlin and Margarita Moreno-Betancur in the latest issue of Statistics in Medicine onlinelibrary.wiley.com/doi/10.1002/... (1/8)

What is common knowledge in your field, but shocks outsiders?

Validated does not mean it works as intended. It means someone has evaluated it (and may have concluded it doesn’t work at all)

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…

**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

People always ask me, “how do I know my manuscript is done?”

There’s only one way, my friends.

If your file name looks something like this:

Manuscript - Final Draft 3.7 FINAL FINAL - FINAL (5).docx

Then, and only then, is it time.

Tempted

Re-proposing the Occam's taser: an automatic electric shock for anyone riding the AI hype train making their models unnecessarily complex

You just don't appreciate modern #dataviz

Rule of thumb: If your model requires data to look like this (balanced after SMOTE), then maybe you want to use a different model