Predictive Analytics Times
Predictive Analytics Times

10 months ago
Why Isn’t Machine Learning More Widely Used for Medical Diagnoses?


Originally published in May 30, 2018

I used to scratch my head asking the same question until I had an enlightening conversation with a close friend of mine who works for a large healthcare consulting company. He led me to the realization that a physician’s agenda is much different than a machine learning practitioner’s agenda, which is much different than a lawmaker’s agenda, which is much different than a hospital administrator’s agenda. In the spirit of full disclosure, I am a medical doctor with a machine learning background, and I use ML on a daily basis to help other healthcare professionals solve problems.

To lay out the problem more concretely, let’s divide the healthcare landscape into 3 broad categories:

  1. Large institutions, such as universities and private hospitals.
  2. Solo physicians in private practice, or small groups of physicians, who are trying to resist the trend toward consolidation.
  3. Healthcare professionals, such as nurses, physical therapists, and administrators, who are just as critical to a successful practice as physicians are.

Healthcare professionals have several goals:

  1. We want to help our patients live healthier lives.
  2. We want to do our work more efficiently. Healthcare professionals often work in high-volume environments and must be perfect, even under time pressure. Electronic medical records are constantly being changed and upgraded, and physicians spend/waste too much time being trained on a moving target. Physicians in larger groups face perpetual pressure to see more patients in less time, while documenting all of their encounters to the T.
  3. Cost reduction. This is important whether we’re talking about solo practices or large academic institutions. This is also important on a societal level, as it pertains to lack of preventative care and resource utilization.

Machine learning promises to help physicians make near-perfect diagnoses, choose the best medications for their patients, predict readmissions, identify patients at high-risk for poor outcomes, and in general improve patients’ health while minimizing costs. This is happening at a rapid pace despite the many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles and the best solutions to those obstacles.

In the early infancy of healthcare informatics, before it was even called healthcare informatics, machine learning algorithms such as neural networks and support vector machines were showed off in publications that predicted the likelihood of malignancy or mortality from a disease. One recent publication that illustrates this is Artificial neural networks and prostate cancer-… [Nat Rev Urol. 2013] from a group at the Charite Hospital in Berlin. This is a great paper, and the Charite Hospital has a tradition of strong interdisciplinary work between physicians and computer scientists; I appreciated this quickly while I was rotating there. However, most physicians have never even heard of machine learning, and most ML practitioners don’t understand the realities of practicing medicine.

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About the Author:


Omar Metwally, M.D., Health Technologist, Meta-Physician, works at the University of Michigan, and studied at University of Michigan Medical School.  He is dreaming up and building solutions to problems like:

  • How can cryptocurrency and smart contracts democratize access to healthcare? (Ethereum)
  • How can we make software minimally invasive? (Amazon Echo, chatbots)
  • What is intuitively designed software? (Natural language interfaces)

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