Rise of the Machines: Progress or Replacing MDs?

Medicine -- including ophthalmology -- has come a long way over the past century, driven by brilliant and committed people and better technology.

In ophthalmology alone, we can see the progress in laser vision correction and cataract surgery over the past 20 years to premium lenses. Treatment of glaucoma is entering a renaissance with a multitude of minimally invasive techniques.

One of the things that drew me to ophthalmology was the wide use of advanced technology. For example, retinal imaging, which utilizes some of the most advanced technologies available (such as adaptive optics imaging) to femtosecond laser-based eye surgeries. Many of my colleagues echo these sentiments.

As we look at this emerging technology, we can ask ourselves: Is medicine (and the greater world) entering a new dawn of artificial intelligence (AI) and technology?

AI holds great promise in medicine. Machine learning and deep learning -- both subfields of AI -- are particularly of interest. In areas such as pathology and radiology, pattern recognition is the basis for making a diagnosis.

As we see in the studies mentioned, machines are exceptional at recognizing complicated patterns at a complexity that only has been possible by humans until now. Furthermore, machines are faster and more consistent without the burdens of work hour rules, overtime pay, or costly benefits.

Ophthalmic diagnosis utilizes pattern recognition extensively. Most ophthalmology diagnoses can be made exclusively with the ophthalmic examination, and more with addition of modern multimodal imaging. This likely means that machine-based diagnostic modalities are well suited for the ophthalmology space.

Machine Learning

In a recent JAMA paper, a Google team used an AI algorithm to interpret and grade fundus photographs with various stages of diabetic retinopathy as accurately as a cohort of ophthalmologists.

The algorithm diagnosis was compared to the majority decision of at least seven board certified ophthalmologists grading over 11,000 color fundus photos. The algorithm attained sensitivity of 97.5% and 96.1% with specificity 93.4% and 93.9% in two image sets. Using an 8% prevalence of referable diabetic retinopathy, these results yield a negative predictive value of 99.6% to 99.8%.

This Google deep-learning algorithm is an advanced artificial neural network, composed of many simple, highly interconnected processors. The nodes or processors within the system make simple calculations that are weighted and added together to produce the final output.

The Google system was trained using about 120,000 color fundus photos diagnosed by ophthalmologists. In the training phase, the system made a diagnostic "guess" on each image.

It then compared its answer to the ophthalmologists' labeled answer and adjusts the algorithm, learning how to compute the lowest possible diagnostic error. It does this again and again, hundreds of thousands of times.

After the training was completed, the algorithm was validated in the study. About 11,000 never-before-seen images (out of sample) were shown to the algorithm, with the results compared to board certified ophthalmologists, which yielded impressive results.

While Google's algorithm may not be the first to have success interpreting diabetic retinopathy images, it is the most extensive and thorough, considering the sheer number of images and the fact that every image was reliably pre-labeled by ophthalmologists.

Humans Obsolete?

As history has shown, an initial response to machine-learning advances in medicine can be one of concern that they will replace physicians.

It is quite true that significant disruptive technology can cause reorganizations in the workplace and physicians are not immune. As physicians, we should look critically into the future to how these assistive technologies will affect us and make adjustments accordingly.

Consider this dire prediction in diagnostic radiology: "They should stop training radiologists now," said Geoffrey Hinton, an AI computer scientist, University of Toronto. Fortunately, this is an extreme statement and does not directly apply to ophthalmology because of the procedural nature of our specialty.

At this point, it is important to understand that these revolutionary technologies were developed to aid clinicians and are neither intended to nor will they replace physicians.

Given that diabetes is one of the fastest growing and leading causes of blindness worldwide, the investigators at Google identified this significant unmet need that physicians and the current healthcare system are not fulfilling.

A widespread deployment of a deep learning-based, diabetic retinopathy-screening program would lower the barriers of access to areas where an eye care provider may not be present. It would determine which patients have pathology, referring on a grander scale, and allow physicians to see more patients with pathology and less healthy patients.

What's Best for Patients

In the long run, this would provide earlier detection of referable diabetic eye disease and decrease overall healthcare costs.

The introduction of assistive, or "smart," screening programs are likely to increase, not decrease, the volume of diabetic eye referrals-patients with real disease that need an ophthalmologist's expertise as a result of capturing a greater portion of the afflicted patient population with diabetic retinopathy. Clearly, they will increase the pathologic workload sent to ophthalmologists for treatment and management, thus increasing efficiency of time and of the healthcare system.

Physicians have asked the question: "Will a machine diagnose as well as a physician?" We are one step closer to knowing that answer and it looks as though the answer may be a resounding "Yes," especially for pattern recognition-based diagnosis.

Today, with machine learning showing promise across society and in areas of medicine, it perhaps is becoming clearer, although with much work to do. We need to ask ourselves, as skilled subspecialists charged with preserving patients' vision, how do we utilize these AI-based systems to provide the best care possible to patients?

This article originally appeared on our partner's website Ophthalmology Times, which is a part of UBM Medica. (Free registration is required.)

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