This is the Trenowin Digest for May 2026. It is preserved here as a permanent record. For the current edition, follow this link to the digest page.
The Trenowin Digest
May 2026
The May 2026 edition of the Trenowin Digest — a round-up of what was published, what was interesting, and what was coming next. Trenowin launched this month. Nineteen articles went live across six content pillars in the first four weeks.
From the editor
May was a sprint. Trenowin launched with five posts in the first week, then accelerated — and by the end of the month nineteen articles were live. That was faster than planned, partly because the research kept turning up papers worth writing about, and partly because the AI Skills series took on a life of its own once the first few posts went up.
The early feedback was positive, but the most useful signal was the questions: what are the categories? Where should I start? How do I find articles on a particular topic? Those questions drove the changes to the site during the month — the pillar pages, this digest, and the orientation strip on the blog. The site became easier to navigate for someone arriving without context, which was the gap.
June was planned to be slower and more deliberate. The AI Skills series was complete. The focus shifted back to the clinical evidence pillars — On-Call & Triage and Clinic & Planning in particular had a backlog of strong papers that hadn’t been covered yet.
Published this month
The AI Skills pillar launched in the second half of May as a structured series on using AI as a personal productivity tool — distinct from the clinical evidence posts. The eight posts cover setup, model selection, voice and memory, the different Claude modes, building AI personas, and workflow automation. They are practical and first-person in a way the clinical posts are not.
If you have been thinking about using AI seriously in your own work and have not found a way in, the 15-minute setup guide is the most useful single post on the site.
A study testing GPT-5, Gemini, and Mistral on hand fracture detection. Mistral was confident and wrong — and the paper named the failure mode cleanly. The issue is not that AI gets things wrong; it is that it can do so with high expressed confidence. The one to read if you are thinking about AI-assisted diagnosis in any acute setting.
A real published AI study with near-perfect reported accuracy — used as a vehicle to explain overfitting, small datasets, and the gap between internal and external validation. A paper reporting very high accuracy from a small dataset is not a reason for confidence. It is a reason to ask harder questions.
A deep learning system detecting cervical cord compression on MRI with performance above mid-level physicians — and two-centre external validation. A good example of a study that does things right: tested in a new population, with a clinical scenario that justifies the development effort.
What was coming in June
The clinical evidence backlog. Several strong papers in draft — on outcome prediction after proximal humerus fracture, LLM performance on fracture classification from radiology text, and patient-facing AI in the consent process. Clinic & Planning and On-Call & Triage material.
One longer essay also planned — around 1,200 words — on the clinical governance question: when an AI tool influences a clinical decision and it goes wrong, what does accountability actually look like?
The AI Skills series was paused. June and July weighted towards the evidence-based clinical pillars.
Site update
The site navigation now includes a Pillars section — six pages, one per content pillar, each explaining what it covers, who it is for, and listing the published articles with brief context. Start at trenowin.co.uk/pillars.
The Imaging & Diagnostics category was retired. The sole post in that pillar (cervical cord compression AI) moved to On-Call & Triage. The category count is six: Foundations, On-Call & Triage, Theatre & Robotics, Clinic & Planning, Career & Practice, and AI Skills.
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