-
📖 Implementing academ.ai, a local retrieval system for academic papers
Semantic and hybrid retrieval for academic papers - look across >100,000 papers and find the relevant ones without losing your mind with keyword-based search.
Curious? Read more
-
📖 Implementing academ.ai, a local retrieval system for academic papers
Semantic and hybrid retrieval for academic papers - look across >100,000 papers and find the relevant ones without losing your mind with keyword-based search.
Curious? Read more
-
📖 LLMs can't innovate
They sure can write gooder than me. But is innovation really their strongest suit? And most importantly - why does that matter?
Curious? Read more
-
📝 Auto-METRICS - a proof of concept
Automatic assessment of methodological quality in radiomics research
Recently, I received my first (obvious) LLM peer review. It was quite blatant. What’s worse: it wasn’t good - at all! Funnily enough, I had been working on something related: Auto-METRICS, a tool for automatic standardised assessment of scientific research quality in radiomics research using the METRICS framework.
To show its utility, we make use of two unique, recent datasets on reproducibility in radiomics studies - Akinci D’Antonoli et al. (2025) and Kocak (2025). Together, they feature really good set of METRICS ratings - for different levels of expertise and training - for more than 50 publications. This allowed us to systematically compare human and LLM raters.
The main takeaways:
- Human raters agree with LLMs at the same rate that they agree with other human raters ✅
- Prompt iterations: clarifying radiomics guidelines can lead to better agreement with human raters. However these improvements were quite limited! 📈
- Too nice: LLM ratings tended to be slightly higher than those offered by human raters 😇
I tested our tool - Auto-METRICS - here (all you need is a free Google Gemini API key) and found it really helpful to get an initial assessment for METRICS which I can easily confirm. The key? Enhance, don’t replace - having good initial ratings was super helpful in getting a final, human-based classification.
Curious? Read more about Auto-METRICS at medRxiv.
-
📝 How to develop a radiomics signature
Presentation and workshop on applying machine-learning to radiology data to medical doctors + biomedical researchers
In 2024, I participated as faculty in the “How to develop a radiomics signature” course organised for the European Society of Gastrointestinal and Abdominal Radiology. Here, I gave a short presentation on machine-learning model development and tuning, as well as two practical programming sessions: one on radiomic feature extraction and the other on model development and (fine-)tuning. Both are freely available.