From the March 2018 issue of HealthCare Business News magazine
By John Vartanian
If you ask anyone working in the medical field today, the words at the top of this page will return a unanimous adverse response as they pose a grave risk and would take away from the personalized care that individual patients need.
This response is due to the fact that these technological innovations directly challenge the status quo that has been the bedrock of clinical medicine for generations of doctors. Physicians are the gatekeepers of medical knowledge and it is their experience and intuition that should be the deciding factors when selecting between treatment options.
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However, many across the medical community have begun to speak out against this dogmatic belief with the understanding that treatment decisions should be based on the best clinical evidence available at the time. While this approach to optimizing patient care feels like common sense, it requires clinicians to ensure that no recent publication could alter their current treatment protocols. This becomes nearly impossible when you consider the 40,000-plus articles published across the 30,000 active scientific journals every week.
With artificial intelligence platforms like IBM’s Watson, able to read thousands of journal articles every day from publications worldwide, we must take a moment to ask ourselves, at what point does it become unethical to NOT use computers in the diagnostic process? If a computer can stay up to date with cutting-edge research from around the globe, should the role of the physician convert from a bank of medical knowledge to a translator of complex medical information for the patient?
This way of thinking brings about new questions as we begin to critically look at other sectors of the medical system that are experiencing pressure from digital diagnostics, specifically radiology and pathology.
The radiology resonance is in full swing as numerous companies such as Zebra Medical Vision work to integrate computer-aided diagnostic algorithms into current clinical practice. These tools use machine learning to help clinicians see into the microscopic details that were previously missed with the human eye alone.
The days of radiologists spending countless hours meticulously scanning thousands of X-rays, CTs and MRs are numbered. Rather than relying on a clinician to catch a life-threatening abnormality among hundreds of images viewed per day, we can now use computers to triage the image bulk.