“The role of the radiologist will be obsolete in five years…there’s no reason a human should be doing [diagnostic radiology].”
-Vinod Khosla, famed health tech venture capitalist
Medicine has come unimaginably far over the last century, driven by brilliant committed people and technology. In the last 20 years, we have seen the introduction of monoclonal antibody drugs, robotic surgery, and astounding intravascular treatments. All of medicine is entering a renaissance with a multitude of minimally invasive techniques and advancements.
As we see the ‘old fashioned’ physical exam go by the wayside as technology supplants and enhances our diagnostics by leaps and bounds. With cheap and plentiful EKG machines, how much less do we rely on a stethoscope? With the introduction of telehealth solutions, sometimes the physical exam is totally forgone.
Is medicine entering a new dawn of AI?
As we look at this emerging technology, we can ask ourselves: Is medicine (and the greater world) entering a new dawn of artificial intelligence and technology? If so, will these AI technologies only assist doctors or will they replace physician in some tasks? What does this mean for doctors, nurses, and the future of medicine? Here are some of the things we are already seeing:
- GoogLeNet AI reviewed thousands of medical images supplied by a Dutch university and was able to identify malignant tumors in breast cancer images with an 89% accuracy rate, compared to 73% for its human counterparts. –Detecting Cancer Metastases on Gigapixel Pathology Images, Google/Alphabet
- A neural network algorithm proves to be more sensitive than experienced radiologists for detecting thyroid nodules in ultrasound imaging. –American Journal of Roentgenology, 2016
- A Google team used AI to interpret and grade retinal images of diabetic retinopathy at least as accurately as a cohort of ophthalmologists. The algorithm diagnosis was compared to the majority decision of at least 7 board-certified ophthalmologists. The algorithm attained sensitivity of 97.5% and 96.1% with a specificity of 93.4% and 93.9%, yielding a negative predictive value of 99.6-99.8%. –JAMA, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs, 2016
There is no doubt that artificial intelligence (AI) holds great promise in medicine. Machine learning and deep learning, sub-fields of AI, are of particular interest. In areas such as pathology and radiology, pattern recognition is the basis for making a diagnosis. As we see in the studies above, machines are exceptional at recognizing ever more complicated patterns, at a complexity that has only been possible by humans up until now. Furthermore, machines are of course faster and more consistent, without work hour rules, overtime costs, or costly benefits. Machines never get tired, distracted, emotional, or careless.
Image Credit: The Doctor Weights In