As AI-assisted tools become increasingly embedded in academic publishing, most major journal platforms now offer automated systems that claim to recommend suitable outlets based on a manuscript’s abstract. But how well do these tools perform in practice? To explore this, we tested five journal finder platforms — four developed by
Integrating Google’s Gemini assistant into Google Docs offers a lightweight yet effective solution for academic and professional editing tasks. Rather than relying on traditional spelling and grammar checkers, users can now issue custom prompts to Gemini—transforming the assistant into a real-time editorial aid capable of producing high-quality revisions
Academic writing increasingly relies on consistent, machine-readable formatting—especially when preparing manuscripts for digital publication, automated parsing, or citation indexing. This post demonstrates how the Mistral Le Chat can accurately convert plain-text bibliographic entries into structured HTML, generating both inline (short-form) citations and full bibliographic records with cross-linked anchors. This
Formatting academic manuscripts in LaTeX can be both laborious and technically demanding—especially when converting raw text into structured, publication-ready documents. This post presents a practical workflow using GPT-4.1 to automate manuscript formatting with remarkable precision. With a single, well-crafted prompt, the model generates clean LaTeX code with proper
Identifying specific clinical findings in unstructured medical texts is a common challenge in healthcare data science. In this post, we benchmark Google’s Gemini 2.5 Flash language model on a zero-shot classification task: detecting the presence or absence of distal fibula fractures in real-world German radiology reports. Without any
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
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
