The diagnostic radiology workflow can be simplified into its component steps as visualised above: from patient presentation and history which leads to decision-making on whether or not to image, and what type of imaging to perform, to scheduling the imaging, and automating or standardising image acquisition. Once imaging is done, algorithms will increasingly post-process images ready for interpretation by other algorithms, registering data sets across longitudinal timeframes, improving image quality, segmenting anatomy and performing detection and quantification of biomarkers. At present, diagnostic reasoning seems the toughest nut to crack and is where humans will maintain most presence. This will be aided by the introduction of smart reporting software, standardised templates and machine-readable outputs making data amenable to further algorithmic training to better inform future decision-making software. Finally, communication of the report can be semi-automated via language translation or lay-translation, and augmented presentation of results in a meaningful form to other clinicians or patients can also be accomplished. And this is only for starters…

While artificial intelligence can absolutely play a part in each of the steps in this diagnostic workflow, and even replace a human in some of them (like scheduling) it simply cannot replace a radiologist entirely. That is unless we miraculously develop a complete end-to-end system that has oversight and control over the entire diagnostic pathway. This to me is a pipe dream, especially given the current state-of-the-art in AI systems which are only just barely making it into clinical workflows at present, none of which are anywhere close to replacing radiologists’ image perception work in any significant sense.

Reason 2. Humans will always maintain ultimate responsibility

In 2017 not one single human being died in a commercial airplane accident. This amazing success story is in part due to the implementation of high-tech systems that automate many of the safety oversight tasks normally conducted by human staff, including, but not limited to, collision avoidance systems, advanced ground proximity warning systems, and improved air traffic control systems. It is also in large part due to better training, awareness of safety issues and alerting/escalation of concerns by human pilots and other ancillary aviation staff.

Where automation has evolved over the past couple of decades, humans have been given more freedom to communicate safety issues, with more time to react to increasing amounts of useful information, all supported by a cohesive environment of industry-led safety awareness. The most crucial fact, however is that there has been zero decrease in the number of commercial pilots — in fact, quite the opposite. Airlines are reporting a shortage of trained pilots, and there are growing concerns over a predicted need to more than double the global number. You see, as safety improves, costs reduce, flying becomes more popular, passenger numbers increase, it stands to reason that more planes will be required.

Medicine is often compared to aviation, sometimes inappropriately and often inaccurately. However, I feel there are some overlapping key features for both industries. For starters, both are focussed primarily on maintaining the safety of humans while getting them from point A to point B, either geographically or systematically. Both also traditionally rely on human expertise and high level training to oversee the processes involved. Both also have seen huge strides in automation over the past decade, and of course both stand to benefit significantly from artificially intelligent systems taking more and more of the cognitive workload and mundane tasks away from humans. But most importantly — in both industries, humans are categorically not being replaced.

The reason is simple — legal responsibility. It is almost unfathomable to imagine the owner of an AI system opting to take full legal responsibility of a machine output when human lives are on the line. No airline has come close to flying a commercial plane entirely without pilots, and if it does, I would bet that the insurance policies will be so huge it will likely not make it worth it for general commercial flying (however, I concede it may be seen on private or military flights, for instance). What we will likely see is ‘drone’ piloting of commercial flights — pilots seated squarely on terra firma but remotely monitoring everything happening on a plane as it soars across the globe. In fact, experiments are already being planned for remote piloting, with mixed reactions from the general public.

In medicine, it is currently far, far, far easier to simply limit an AI system to providing ‘decision support’ and leave all ultimate ‘decision-making’ to a qualified human. Not one single existing AI system that has medical regulatory approval has yet claimed to be a ‘decision-maker’, and I sincerely doubt that one ever will, unless the decisions being made are minor and unlikely to be life-critical. This is because it is impossible for an AI system to ever be 100% accurate in solving a medical diagnostic question, because, as I have previously discussed, medicine in part still remains an art which can never be fully quantified or solved. There will always be an outlier, always be a niche case, always be confounding factors. And for that reason alone, we will always need some form of human oversight.

Reason 3. Productivity gains will drive demand

“If you build it, they will come” is the often misquoted saying from the movie Field of Dreams (or Wayne’s World 2, depending on your generation). If we build systems that massively improve radiology workflow and diagnostic turnaround, we will almost certainly see a massive increase in demand for medical imaging.

I’ve seen this with my own eyes — when I was a trainee, our department started a new initiative to try and reduce waiting times for ultrasound lists. We opened up an evening list with three or four extra slots for urgent walk-in patients or those that had been waiting more than 3 weeks. At first, this worked out nicely, with one trainee being assigned per day to this extra list. It only took an hour maximum after all. Fairly soon however, we started noticing requests coming in saying ‘for the extra list please’, and before we knew it we had to start opening up extra-extra lists, and extra-extra-extra lists, which in turn just became the new normal. My point here is that in radiology, if you offer a doctor a slot to scan a patient, they will find a patient to fill that slot!

As AI becomes the new normal in radiology, as scan times and waiting lists reduce, and as radiology reports become more accurate and useful, we will continue to see an increase in demand for our services. Add to this the ever increasing population growing in age and complexity, it is to me 100% inevitable that demand increases, and probably the major reason why I remain bullish on radiology as a career choice.

We will need to train more radiologists to combat the tidal wave of imaging being requested and data being produced, and may even consider dual or triple accreditation in other data-producing specialities such as pathology and genomics. ‘Radiologists’ may not even be called radiologists in the far future — at least that’s one theory I heard talked about at RSNA last year, but that doesn’t negate the fact that someone human will still be in control of the flow of data.

What will radiologist’s be doing then?

The radiologists of the next few decades will be increasingly freed from the mundane tasks of the past, and lavished with gorgeous pre-filled reports to verify, and funky analytics tools on which to pour over oceans of fascinating ‘radiomic’ data. It won’t quite be like Minority Report, but if you want to imagine yourself as Tom Cruise swiping and gesturing away at a screen of futuristic malleable real-time data, then go right ahead.

Where radiology artificial intelligence is heading towards is digital augmentation of radiologists, to the point at which their job becomes to monitor and assess machine outputs, rather than manually go through every possible mundane finding as they do now. Personally, I welcome this with open arms — I have wasted far too much of my working life measuring lymph nodes on multiple CT scans or counting vertebrae to report the level of a metastasis. I would much rather check a system has measured the correct lymph nodes and identified all the vertebrae required, and sign off on the findings. Radiologists are going to be transformed from ‘lumpologists’ with crude tools, to ‘data wranglers’ dealing with ever more sophisticated quantified outputs.

Radiologists will also be empowered to become more ‘doctor’ than ever before, with productivity gains allowing more time communicating results to both clinicians and patients. I can certainly envisage radiologists as data communicators, both directly to clinical teams on their rounds and tumour boards, and even direct-to-patient information-giving. The profession at the moment is only harmed by too much hiding away in dark rooms and, if anything, artificial intelligence has the capability of bringing radiologists back out into the light. That’s where it’s true power lies.