Doctor Watson will see you now


Will your next x-ray be read by an algorithm? Will artificial intelligence replace radiologists? Merge Healthcare seems to think so. Technological advances in deep learning combined with the digitization of medical images has created one of the largest opportunities to disrupt the practice of radiology.

What is Radiology

Radiologists diagnose and treat diseases and injuries through the use of medical imaging techniques such as x-rays, CT scans, and MRIs. The role of a radiologist is to function as a consultant to the referring physician by helping choose the appropriate diagnostic tests, interpreting the medical images, and recommending further treatment as needed (1).

The Digitization Revolution

Over the last 10 years the field of radiology has transitioned away from physical film towards digitized medical images. Much like Kodak and the advent of digital photos, many companies that manufacture x-ray equipment or physical chemicals/film have gone bankrupt due to their inability to adapt to an increasingly digital world. Digital records disrupted the specialty by enabling remote access to patient records, removing physical barriers to sharing and storing information, and delivered better quality / higher resolution images that improved diagnosis. From a process perspective, the digitization of medical images improved workflows, increased productivity in terms of number of patients seen per day, eliminated the costs of chemicals and film, and reduced error rates (2).

However, the downside of digitization is that the amount of information a radiologist has to manage has grown exponentially. The number of images radiologists have to interpret has increased 100 fold in the last 20 years with some radiologists reviewing 20,000 studies per year (3). Doctors now have to process increasingly more detailed and complex images while trying to simultaneously cross-reference a patient’s medical history, lab data, and the latest medical research to make the most accurate diagnosis.

Overview of Merge

Merge Healthcare sits at the center of the technological revolution in radiology. Merge is one of the largest providers of imaging processing software with the largest database of digital medical images having processed 30 billion images to date across 7,500 healthcare facilities in the US (3). The company’s technology delivers value to its users, who are largely physicians and other healthcare providers, by creating a common platform that enables radiologist to view, share, and interpret clinical images in a more productive, accurate manner.  As a result of its unique access to a large share of US radiologists and its vast library of historical images, Merge has the opportunity to be at the forefront of the next major disruptive technological innovation in the industry: machine learning.

Machine Learning: The Opportunity

Machine learning has the potential to meaningfully improve the speed and accuracy of medical diagnosis by processing vast amounts of imaging data more quickly and accurately than a physician. In a world where a typical trauma patient’s “pan scan” results in over 4,000 images it’s no wonder artificially intelligence is better equipped to avoid missed or inaccurate diagnosis resulting from visual fatigue (4). Deep learning algorithms can deliver value by improving both image processing and image interpretation. On the processing front, machine learning algorithms can help cut through the noise and extract the most relevant features from medical images as well as cross references images with a database of scans and the latest medical research. On the interpretation front, deep learning can not only improve the identification, classification, and quantification of disease patterns from images, but also generate predictive insights into the most relevant care pathway (5).

The Next Evolution of Merge

In 2015, Merge was acquired by IBM for $1bn in a move that cemented the shift in its business model away from purely serving as an imaging workflow platform towards the development of a sophisticated smart diagnosis IT service for radiologists (3). By leveraging Merge’s platform and database with IBM Watson’s machine earning algorithms, the combined business hopes to disrupt the field of radiology by offering IT solutions that can dramatically improve a doctor’s ability to accurately diagnose and predict diseases. IBM Watson/Merge’s foray into medical imaging AI has the potential to disintermediate the profession of radiology, raising important ethical and regulatory questions about machines ability to make medical decisions. From a business model perspective, Watson/Merge should carefully consider the scope of the initial product and its positioning to clinicians. I would focus on selling a more “assistive” version of the software that enables clinicians to make better decisions rather than one that generates independent diagnosis. For example, the software should pre-highlight key areas of concern in an image and synthesize relevant takeaways from the image database or literature – leaving the ultimate medical decision to the doctor. A major barrier to the commercial success of the product will be the physician adoption rate so designing a solution that physicians can trust without worrying about disintermediation will be critical.



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Student comments on Doctor Watson will see you now

  1. Great article Pavitra! From reading about TOM IBM Watson Jeopardy case to now Radiology and the disruption Merge is bringing into the healthcare sector is pretty amazing. Truly machine learning has opened up flood gates into many areas.

  2. Very interesting discussion on how machine learning algorithms can be applied to interpret medical images! I find it particularly interesting that Merge can do this not only more quickly but also more accurately than doctors. With this being said, I wonder whether there is the potential to use this technology as a means by which to reduce healthcare costs? The reading fee associated with an MRI can range from $100-500 ( If this can be reduced through utilizing Merge technology, it would provide immense value to patients. As you discussed, radiologists do not want to be intermediated. However, if technology allows for decreased demand of their services, it is not fair to expect patients to continue to pay higher healthcare costs in order to keep radiology staff levels at the same level they are today. Hospitals need to remember to put patients, not doctors, first.

  3. I really enjoyed this read! Although the thought of AI replacing radiologists is alarming, it does make a lot of sense. I found the statistic you referenced in the post rather astounding… “the number of images radiologists have to interpret has increased 100 fold in t he last 20 years”. This seems rather unsustainable, yet I do not see this slowing or changing. I worry about the lawsuits / backlash that might stem from a misdiagnosis or delayed diagnosis when we add the AI element to the mix. It will be very interesting to watch this unfold!

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