We assessed the ability of Claude 4 Sonnet and GPT-4o to classify brain MRI images as healthy or tumorous for research labelling purposes, using both the chat interface (10 images) and the API (125 images). Claude 4 Sonnet achieved perfect accuracy (10/10) in chat, but its API refused to
Prompt-based methods are becoming increasingly relevant in biomedical text mining, offering flexible ways to perform tasks such as named entity recognition without explicit model training. In this case study, we assess the performance of DeepSeek-V3 on a structured disease mention extraction task using a curated subset of the NCBI Disease
Accessing large language models (LLMs) through APIs opens up research and development opportunities that go well beyond the limits of traditional chat interfaces. For researchers API access enables language models to be built directly into bespoke workflows, analytical tools, or automated processes—supporting tasks such as large-scale text analysis, data
Recent advances in open-weight large language models have made it possible to run powerful AI tools entirely on local machines. In this article, we outline how researchers can set up and interact with models such as Deepseek, Mistral, or Llama using Ollama for local model management and Open WebUI for
How well can today’s leading GenAI models classify real-world biodiversity data—without bespoke code or traditional machine learning pipelines? In this study, we benchmarked a range of large language models on the task of predicting penguin species from tabular ecological measurements, including both numerical and categorical variables. Using a
How far can today’s large language models go in scientific data analysis—without bespoke coding or deep learning pipelines? In this experiment, we explore the ability of DeepSeek-V3 to perform pixel-level detection of Argania trees (i.e., binary classification for each pixel) using only tabular Sentinel-2 spectral data. By
Working with real-world text data often means dealing with structures that are not yet analysis-ready. In our case, the dataset included headlines and full article texts stored separately, across two different tables. The only link between them was a shared identifier field: Body ID. Before we could begin any further
Image-based classification is increasingly used across biology, ecology, and agriculture—from identifying animal species to detecting plant diseases. One common use case is the analysis of leaf images to distinguish between healthy and diseased plants. In this post, we compare two approaches to classifying strawberry leaves as either fresh (healthy)
This post presents a practical solution for integrating OpenAI’s GPT API into RStudio using a custom Shiny interface. The tool enables real-time code generation from natural language instructions, allowing users to interact with GPT-4 directly within their R workflow—without leaving the environment or blocking the console. We demonstrate
Working with biomedical corpora often requires programming skills, specialised formats, and time-consuming preprocessing. But what if you could transform a complex annotated dataset—like the NCBI Disease Corpus—into a structured, analysis-ready CSV using nothing more than a single, well-designed prompt? In this post, we demonstrate how a no-code, GenAI-powered
Large language models (LLMs) rely on a set of parameters that directly influence how text is generated — affecting randomness, repetition, length, and coherence. Understanding of these parameters is essential when working with LLMs in research, application development, or evaluation settings. While chat-based interfaces such as ChatGPT, Copilot, or Gemini typically
As generative AI technologies continue to transform the research landscape, major academic publishers are beginning to integrate AI-powered tools directly into their platforms. These tools aim to support researchers at various stages of the scientific workflow – from literature discovery and summarisation to writing assistance and experimental comparison. This article provides
