> Why couldn't a model report all discernable co-morbidities?

It could, but it's not there yet. I was trying to illustrate the enormous chasm which AI has yet to cross. Even if an AI model is trained to interpret all head CT pathologies, that is an absolutely minuscule part of practicing medicine.

Let me be clear that I agree that many of the functions of doctors are theoretically replicable with technology. I just have radically different view to the timescale that this will be on compared to many HN-ers who seem to equate treating patients with analysing a computer program, a comparison which is woefully inadequate.

The advances in image interpretation AIs in medicine are a bit misleading as they are literally the lowest of the low hanging fruit. There are huge amounts of data to mine with both normal results and pathological ones, and the data is already in a relatively consistent and nice format for the model to be trained on.

And yet it's 2020 and we don't even have accurate computerised diagnostics for ECG interpretation which is essentially 12 arrays of floats.

I relish the advances in tech, but the chasm between what "robo-doctors"/"AI" etc can actually achieve right now to benefit patients and what a doctor even just out of medical school can do on a day to day basis is vast. The progress in "potential doctor replacements" we have seen from the technology sector is hugely hyped but realistically has a minuscule effect on patient outcomes at present.

I understand people become very frustrated with inadequate healthcare systems and especially when mistakes are made. The go-to answer of "doctors are scumbags, they don't do anything anyway and AI will replace them in a decade" is facile and ill-informed.

Yesterday I walked past a patient who was vomiting fresh blood. She needed urgent wide bore IV access, bloods, blood transfusion, review from upper GI surgeons and head and neck surgical oncology and immediate return to theatre to open up her neck and explore what was going on to hopefully fix it. There is not the slightest hint of a technological solution to this managing this single random example of which I could have picked many thousands more.

Last week I was called to ED to review a patient who had had an industrial accident with heavy machinery and had an almost complete degloving of his arm and almost complete amputation of the same. The diagnosis is easy in this case, but which software is going to keep the man alive and try and save his arm?

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Diagnosis may not be "creative" but as another commenter points out it is nuanced. I've commented about this before but the main challenge with tech for diagnosis is data collection. It's easy to train and run models on numerical data such as CT scans and blood results because the data collection is easy. Even then the tools we have are pretty useless at present. In my hospital we have an automatic warning if the system suspects a patient has sepsis, which is great. However, it's also often wrong. Which, were it allowed to and able to manage the patient would be a complete disaster. ECGs provide automated interpretation which are usually complete BS.

However, patients are not numbers, or programs; eliciting the information you need to make the diagnosis is the hardest part and that is significantly more challenging than a flowchart. E.g. patient found unresponsive at 3am by a canal, no further info. Where is the AI solving this right now?

Once you have a diagnosis, or at least a working diagnosis, you then need to be able to actually instigate the management for that patient. I have yet to see anything that can automatically take blood, cannulate a patient, intubate a patient, perform a ring block with local anaesthetic, run a cardiac arrest, etc.

The chasm looks small from a distance but when you get up close, it's actually really big.