In the late 1990s, a small group of emergency doctors started carrying portable ultrasound probes.
Most other doctors saw it as a novelty—a diagnostic toy. Imaging belonged to radiology. That was the system.
The early adopters didn’t argue. They just improved. They ruled out pneumothorax, guided central lines, and assessed cardiac function without delays. They made faster, more accurate decisions than the rest.
Once others caught up, POCUS was a core FRCEM skill. The early adopters, now teachers, had quietly gained a lasting advantage—in skill, reputation, and career.
This moment of transformation isn’t unique. A parallel is unfolding as AI reshapes clinical practice.
The literacy gap in medicine
Most NHS clinicians currently sit in one of two places on the AI adoption curve.
The first group: avoidance. They’re sceptical, overwhelmed, or just too busy. AI feels like a technological problem, not a clinical one. They’ll get to it eventually.
The second group: surface use. They’ve tried ChatGPT. They ask questions. They treat it like a smarter Google. They get generic answers and conclude it isn’t that useful.
A third, small but growing group uses AI differently—building workflows, automating tedious tasks, and redirecting saved time to high-impact work.
Key point: the main difference between surface AI users and those who build with AI is not technical skill, but a change in mindset about what AI can accomplish.
What “using AI properly” actually means.
Here’s the distinction that matters.
Prompting AI for answers is like using a GPS to get your current location. It gives you information. It’s useful. But it doesn’t change how fast you travel.
Building AI systems is like designing a better road—everyone benefits, speed is structural and repeatable.
The doctors getting genuine leverage from AI are doing things like:
- Systematising their documentation — dictating clinical notes and having them reformatted, structured, and completed in seconds rather than minutes
- Building content engines — turning one idea into a newsletter, a LinkedIn post, a blog article, and a short-form video script in a single workflow rather than four separate sessions
- Running literature searches and synthesising evidence — reading 15 papers in the time it previously took to read three, and extracting the specific clinical implications rather than wading through methods sections
Takeaway: To fully benefit from AI, shift your approach from asking questions to building repeatable solutions that transform your daily work.
The compounding advantage
Allow me to be specific about what this looks like in practice.
A doctor who saves 90 minutes per week through AI-assisted documentation and admin has recovered 78 hours over the course of a year. That’s nearly two full working weeks — returned without taking a day of annual leave.
If they spend recovered time on something that compounds—like building an audience or developing a course—the return far exceeds another locum shift.
The locum shift pays once. The audience continues to grow over time.
Over three years, the doctor using AI converts efficiency into permanent progress, redirecting their time toward growth. The other retains the same income and has a slightly worse back.
There’s another layer. AI tools are improving at a rate that disproportionately rewards early learners. Doctors who built real fluency in 2024 and 2025 will find the 2026 and 2027 versions dramatically more powerful — and they’ll already know how to use them. The learning curve compounds. Those starting from nothing in two years’ time will be further behind, not closer.
Main takeaway: The divide between early and late AI adopters in medicine is rapidly widening, with accelerating benefits for those who act now.
Three things you can do with AI right now
Not theory. Actual starting points.
1. Clinical documentation
If you’re spending more than five minutes per patient writing up notes, there is a faster way. Audio-to-text tools, combined with a well-designed prompt, can turn a 60-second verbal dictation into a formatted, professional clinical record in seconds. Tools like Heidi Health and similar platforms are already being piloted in NHS settings [check current NHS-approved clinical AI documentation tools available in your trust — check IG policies before using any tool involving patient data]. The time saving is real and immediate.
2. Research and evidence synthesis
Use AI tools to extract the clinical question, study design, outcome, and one clinical implication from each paper. For FRCEM prep or clinical practice, this boosts efficiency through reducing the time per paper by 80%.
3. Content creation
If you’re building a professional presence — writing, teaching, thought leadership, a newsletter — AI reduces production costs. One structured idea becomes a blog post, a LinkedIn post, and a three-email sequence. The constraint stops being “I don’t have time to create content” and starts being “I don’t have enough ideas.” That is a much better problem to have.
Content compounds. A post today gains readers months later. Audiences grow year on year with the same effort spent up front. That isn’t true for a locum shift.
What to do this week
- Find your most time-consuming weekly admin task. Use it as your first AI workflow experiment.
- Spend an hour learning to prompt –give the AI a role, context, format, and constraint. Quality improves immediately.
- Pick one of the three use cases above and run a two-week experiment. Don’t evaluate AI in general. Evaluate it for that specific task.
- Track the time — not loosely, actually count it. If you can’t measure the return, you won’t see it clearly enough to build on it.
- Final takeaway: Deliberately choose how to invest your recovered time so it compounds into lasting career development.
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This post is for educational purposes only and does not constitute financial advice. Always do your own research and, if needed, ask for advice from a qualified financial adviser regulated by the FCA.
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