Build the figures your manuscript needs
Set up a working AI environment, define safe boundaries, and generate the graphs and posters a journal or congress expects — reproducible, not one-off.
Practical online training · first edition
AI for medical research: from question to finished manuscript
Not another prompting course: the same AI gives very different results depending on how you work with it. You will learn to use it the way a publishing researcher does — inside a real working environment, no coding required — to turn a clinical question into a finished manuscript or thesis chapter, statistics included.
José Daniel Subiela
MD, PhD · Consultant uro-oncologist and clinical professor
Jesús López
Applied AI, data and instructor
400 € · refundable deposit of 50 €
Reserve for €50
€400 in total, reserved with a €50 deposit refundable until 30 October · Fri 13 (5–8pm) & Sat 14 November (10:30am–1:30pm) · 15 seats.
Immediate application
Everything your manuscript needs, built in two sessions.
Write a defensible discussion section
Move from a source manuscript to a traceable critical reading, an evidence matrix and a synthesis you can cite directly in your own manuscript.
Produce the descriptive table your manuscript needs
Build a collection sheet, harmonize sources and get the baseline characteristics table — with the right statistical tests — ready to paste into the manuscript.
Write conclusions you can defend
Work through survival analysis and surgical learning curves, with explicit validation, bias and interpretation criteria — the statistics behind your discussion section.
Who it is for
For people who turn medical questions into knowledge.
You do not need to code: you need a question, judgement in your field and the willingness to work with a verifiable method.
01
Clinicians
Practising physicians and residents who want to turn clinical questions into structured, reviewable work.
02
Clinical and biomedical researchers
People who review literature, design studies, build registries or prepare data for analysis and publication.
03
Postgraduate students and PhD candidates
Master's students, PhD candidates and early-career academics developing theses, manuscripts or research projects.
Cases from clinical research and practice
Explore four real examples of what you will learn to build.
Four demonstrations of the kind of work our most applied participants will walk away able to build, drawn from José's own clinical and research practice.
Featured demonstration
From a source manuscript to a reviewable evidence matrix
José frames the clinical question, Jesús builds the workflow with AI, and both review what the system can and cannot conclude.
Video in preparation01:24
clinical-review.logTRACE · ON
01 · QuestionPICO estructurada
02 · Extraction12 variables
03 · Review3 alertas
PopulationMIBC · cT2–T4a
OutcomeOverall survival
LimitationSelection bias
Human validation before accepting conclusions
Network meta-analysis
Compare perioperative treatments in muscle-invasive bladder cancer
Three relevant regimens have no single direct trial that answers how they compare in efficacy and safety.
- Deliverable
- Interactive indirect comparisons, treatment rankings, safety overview and trial table.
- Validation
- The artifact separates efficacy, safety and the limitations of indirect evidence.
Course program
Two live sessions, one publication-ready workflow.
Two 3-hour sessions (Fri 13 & Sat 14 Nov), each with explanation, guided practice and a reusable deliverable. Every block ends with a piece of your manuscript — a table, a figure set or a synthesis.
Recorded onboarding from 6 Nov, plus daily office hours (9–13 Nov) to unblock setup and questions. All sessions recorded for participants.
Video walkthrough Explanation + video Practice + video Explanation Practice
01
Claude Code Setup
Install and configure Claude Code for AI-assisted development
- 1.1 Installing Claude Code Video walkthrough 5 min
- 1.2 Authentication Video walkthrough 5 min
- 1.3 First Steps Video walkthrough 5 min
02
VS Code Setup
Install and configure Visual Studio Code for AI-assisted development
- 2.1 Installing VS Code Video walkthrough 5 min
- 2.2 Creating folders and files in VS Code Video walkthrough 5 min
- 2.3 Installing uv Video walkthrough 5 min
A collaboration tested on real medical data
Years building medical ML models together, now accelerated with AI.
José and Jesús were validating models with clinical data before today's AI. The course turns that into a method you can apply without losing traceability or clinical judgement.
Teaching team
José Daniel Subiela
MD, PhD · Consultant uro-oncologist and clinical professor
Consultant uro-oncologist and kidney transplant surgeon at Hospital Universitario Ramón y Cajal, and clinical professor at Universidad de Alcalá. His research combines real-world clinical data, biostatistics and artificial intelligence, including survival models, XGBoost and causal inference.
Jesús López
Applied AI, data and instructor
Founder of datons and LinkedIn Learning instructor. Years turning medical data into reproducible analyses, tools and models.
First edition
A format built for practice.
- Format
- Two live online sessions · 6 h total
- Live sessions
- Fri 13 Nov (5–8pm) & Sat 14 Nov (10:30am–1:30pm), 2026
- Onboarding
- Step-by-step install videos, available 6 November
- Office hours
- 1 hour a day to unblock setup and questions, 9–13 November
- Recordings
- All sessions recorded and included
- Group
- Maximum 15 participants
- Reservation
- €50 deposit, refundable until 30 October; €350 balance due by then (€400 in total)
- Certificate
- datons certificate of completion. This is a private course: it does not award continuing medical education credits
- Grants
- A few sponsored seats for residents; write to hello@datons.com before reserving
- Price
- 400 €
- Reservation
- 50 € Refundable and deducted from the final price
Registration
Reserve your seat
Reserve with a €50 deposit, refundable until 30 October and deducted from the €400 price; the €350 balance is due by that date to keep your seat. We work only with synthetic or publication-derived data, never real patient data.