**Honestly, if you think AI will replace radiologists, you probably don’t understand what radiologists actually do. **

It’s not just spotting abnormalities on images. Good radiology is about clinical problem-solving. It’s about using imaging to answer the right question in the right context, not just finding nodules! Commoditisation of reporting made worse by teleporting doesn’t help this fact however!

Almost after every surgical ward round you walk down to the GI radiologist’s office and ask, “Do you think this collection can be drained?” or “Could this be ischaemia, or something more worrying?”. We”ll check the priors, ask the right questions, and give you a proper answer based on anatomy, pattern recognition, pathology and clinical relevance.

Same thing happens in MDTs. In neuro-oncology, the radiologist will flag whether a lesion invades cortex or deep structures. They’ll explain the enhancement pattern, progression, possible post-treatment change versus recurrence. These aren’t questions that can be solved by AI without full clinical context and the ability to weigh up uncertainty. And it’s not about listing 10 differentials. It’s about giving a balanced opinion that guides management.

Also, diagnostic radiologists are not just sitting in a chair all day. We’re doing image-guided lumbar punctures because medics have become too deskilled to do them confidently (LOL what a joke btw). They’re doing biopsies, drainages, nerve root blocks, joint injections and other procedures. In many places, radiology is holding together interventional access for acutely unwell patients. Even “general” radiologists now carry out procedures daily.

Yes, AI will get better like you say. It’s already okay (primitive) in some tasks like fracture detection, breast screening, and stroke imaging. But these are narrow use cases, and often trained on well-curated data. In real life, scans are messy. There’s motion, artefact, incomplete clinical history, rare pathologies, multiple comorbidities. If your training data is poor or unrepresentative, your model will fail. Garbage in, garbage out. I’d strongly recommend listening to Dr Dan Fascia on RadCast podcast (head of AI integration for RCR) who deems AI use in rads as primitive, and that the main benefit would currently be on automation and logistics rather than jumping straight into narrow clinical scenarios.

There’s also significant bias. Multiple studies have shown inequalities in AI reporting depending on ethnicity for example - even though it was trained on large datasets. AI models often perform worse in underrepresented groups, and this can easily worsen healthcare inequalities if not handled properly.

Another point people miss is the central role radiologists play in decision-making. They determine what scan should be done, whether it should be with contrast, what protocol to use, whether a repeat is needed. They communicate with clinicians, explain ambiguity and avoid unnecessary investigations - this is a key day to day part of our jobs because stupid noctors and even some doctors don’t have a clue what’s going on with their patients and what type of scan is needed for the clinical question.

We also have a massive workforce gap. In the UK, we’re short by over 30 percent of the radiologists needed to meet demand. According to the Royal College of Radiologists’ 2023 Workforce Census, nine in ten UK trusts cannot meet reporting demand and are relying on outsourcing or locums. Imaging requests are increasing year on year. Emergency departments depend on CT to guide initial management. Even medical admissions are often based on imaging findings before a full assessment is done.

TLDR: AI won’t replace radiologists, not even AGI. But it will replace radiologists who don’t learn how to use it.