The 5 Levels Of Automation In Medicine

“Good morning! How may I help you today?” asks the virtual assistant as you boot your telemedicine app. After experiencing a sore throat and runny nose for a few days, you’ve decided to seek medical attention. You share your symptoms with the assistant who subsequently suggests a cause after scanning its database. “There’s an 83% chance that you are experiencing allergic symptoms,” replies the chatbot. “I will send you your prescription shortly, but if you are not satisfied or still feel unwell, please request for a human physician.”

Considering the likelihood of the diagnosis and the deductive prowess of the artificial intelligence, you decide to heed to its advice and take the prescribed medicines. However, after two weeks, the symptoms persist and you decide to turn to an ENT specialist. After checking your CT scan, the latter determines that you have chronic sinusitis, which will require a surgical procedure.

Had it been diagnosed earlier, you might not even have needed surgery. So who is liable in this hypothetical scenario of the future? The algorithm that suggested allergies rather than an ENT examination, the doctor for not supervising the chatbot or the patient? Going further, what if an algorithm misses a cancerous lesion on an X-ray; or if a surgical robot accidentally damages a nerve bundle and partially paralyses the patient?

With A.I. and its potential to automate processes in medicine, such questions will become commonplace. While it’s healthy to ask oneself such questions, it can prove difficult to find proper answers and get the bigger picture of how automation will impact healthcare.

The spectrum of automation in medicine

In the February issue of The Batch, the newsletter from deeplearning.ai, the website’s founder, Andrew Ng, considered 5 levels of automation in medicine. Other experts have a similar classification system, albeit with different terminologies. The aim is nevertheless the same: to depict in a concise and easy-to-grasp way how the human-A.I. collaboration will unfold in the field of medicine in the years and even decades to come. Below is such an infographic that helps in visualising the spectrum of automation in medicine:

“Today’s algorithms are good enough only for certain points on the spectrum in a given application,” writes Andrew Ng. “As an A.I. team gains experience and collects data, it might gradually move to higher levels of automation within ethical and legal boundaries.”

To help you get a hold of the upcoming waves of automation in medicine, this article will expand on those 5 levels with current examples and imagined scenes that will become commonplace in the years to come.

The 5 Levels of automation infographic

1. Humans only: no A.I. involved

In level 1 of this spectrum, no A.I. is involved. Any medical procedure that has data in it (or not) can be in this category. Humans are doing the work at this base level whether it’s manual work or inputting data to generate a process. These can also involve simple algorithms but not artificial intelligence. 

We don’t need to go far to imagine this stage as most medical procedures are currently done manually. Whether it’s a surgeon performing a laparoscopy or a medical researcher gathering data for a meta-analysis, humans are at the forefront with no assistance from an A.I.

2. Shadow mode: the physician teacher and the A.I. student 

In medical schools, students learn the tricks of the trade by following a designated physician around the hospital. They take notes, ask questions and can perform some physical examinations under the physician’s supervision. This is commonly known as shadowing.

A.I. can undergo a similar “training” process with a so-called “shadow mode”. For example, while a physician makes a diagnosis based on an X-ray, a “trainee” A. I. follows the process without interfering with it. The algorithm thereby takes notes, checks the physician's accuracy and logs everything that can support a future diagnostic decision made by the A.I. itself. This can be used to further develop A.I. technologies that will move them along the automation spectrum.

Already in 2020, researchers from Imperial College London proposed a framework that evaluates the accuracy and uncertainty of human clinicians against that of A. I. in shadow mode’s recommendations. This can help determine how efficient the A.I. “student” is and where it needs improvement to help it move to the next stage.

3. The A.I. assistant 

At this stage of the automation spectrum, the A.I. system supports physicians in clinical decision-making via suggestions. For example, after scanning a database of chest CT scans, the A. I. considers the chest CT results of a patient being investigated and highlights suspicious signs. These signs are then further investigated by the physician.

Watson for Oncology aims to help cancer patients and their doctors stay ahead of the rapid succession of discoveries in cancer treatment. It does so by identifying potential treatment plans for individual patients by combining data from that patient’s medical record and the latest available treatments.

A prospective study published in the Journal of Clinical Oncology in 2019 showed that input from Watson for Oncology led to a change in the decision-making of a multidisciplinary tumour board in 13.6% of cases.

4. Partial automation 

With partial automation, an A.I. system can come up with its own diagnosis; but if it's not confident enough about it, the A.I. turns to physicians for help. Several companies are working on such solutions today.

The A.I.-based system from Behold.ai, red dot, classifies chest X-rays and localises its findings. It can even identify abnormal chest X-rays of COVID-19 patients. It can help in ‘instant triage’ to accelerate diagnosis and allocate resources accordingly.

Palo Alto-based Nines developed an A.I.-system that can identify potential cases of intracranial haemorrhage and mass effect from CT scans. It then flags those cases for radiologists to review.

5. Full automation

As the name suggests, full automation processes are performed by an A.I. alone and do not involve human input. For example, a Level 5 system could analyse a mammogram on its own and request for subsequent testing without consulting a human physician for this decision. Similarly, some scientists speculate that some ophthalmological surgeries can be fully automated since some are already partially automated. 

Nevertheless, some researchers believe that reaching Level 5 automation in any medical setting is “unlikely to be safely achieved in the near term.” So we have to think of it as a long-term eventuality, but such levels of automation fuels fears of A.I. replacing physicians. However, it is more likely that such A. I. systems will excel at a specific task and healthcare professionals will increasingly interact with them. As such, it is more likely that those physicians who use and embrace A. I. will replace those that do not, rather than A.I. alone replacing physicians altogether.

Even though automation in medicine is only taking its first steps, it’s a spectrum that we are steadily progressing across. As such, it’s important that we think about the relevant possibilities lying ahead in medicine. We hope that this article helped you think along those lines and provided a clearer picture of automation in medicine.

Dr. Bertalan Mesko, PhD is The Medical Futurist and Director of The Medical Futurist Institute analyzing how science fiction technologies can become reality in medicine and healthcare. As a geek physician with a PhD in genomics, he is a keynote speaker and an Amazon Top 100 author.

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